Repository: illidanlab/Simulator
Branch: master
Commit: d99e2b1352a1
Files: 17
Total size: 181.5 KB
Directory structure:
gitextract_vka5t1u7/
├── .gitignore
├── LICENSE
├── README.md
├── algorithm/
│ ├── IDQN.py
│ ├── __init__.py
│ ├── alg_utility.py
│ ├── cA2C.py
│ └── cDQN.py
├── run/
│ ├── run_IDQN.py
│ ├── run_baseline_nopolicy.py
│ ├── run_cA2C.py
│ └── run_cDQN.py
├── simulator/
│ ├── __init__.py
│ ├── envs.py
│ ├── objects.py
│ └── utilities.py
└── tests/
└── run_example.py
================================================
FILE CONTENTS
================================================
================================================
FILE: .gitignore
================================================
## Core latex/pdflatex auxiliary files:
*.aux
*.lof
*.log
*.lot
*.fls
*.out
*.toc
*.fmt
# python scripts
*DS_Store*
*.idea/*
*.pyc
*.ipynb_checkpoints*
## Intermediate documents:
*.dvi
*-converted-to.*
# these rules might exclude image files for figures etc.
# *.ps
# *.eps
# *.pdf
## Bibliography auxiliary files (bibtex/biblatex/biber):
*.bbl
*.bcf
*.blg
*-blx.aux
*-blx.bib
*.brf
*.run.xml
## Build tool auxiliary files:
*.fdb_latexmk
*.synctex
*.synctex.gz
*.synctex.gz(busy)
*.pdfsync
## Auxiliary and intermediate files from other packages:
# algorithms
*.alg
*.loa
# achemso
acs-*.bib
# amsthm
*.thm
# beamer
*.nav
*.snm
*.vrb
# cprotect
*.cpt
#(e)ledmac/(e)ledpar
*.end
*.[1-9]
*.[1-9][0-9]
*.[1-9][0-9][0-9]
*.[1-9]R
*.[1-9][0-9]R
*.[1-9][0-9][0-9]R
*.eledsec[1-9]
*.eledsec[1-9]R
*.eledsec[1-9][0-9]
*.eledsec[1-9][0-9]R
*.eledsec[1-9][0-9][0-9]
*.eledsec[1-9][0-9][0-9]R
# glossaries
*.acn
*.acr
*.glg
*.glo
*.gls
# gnuplottex
*-gnuplottex-*
# hyperref
*.brf
# knitr
*-concordance.tex
*.tikz
*-tikzDictionary
# listings
*.lol
# makeidx
*.idx
*.ilg
*.ind
*.ist
# minitoc
*.maf
*.mtc
*.mtc[0-9]
*.mtc[1-9][0-9]
# minted
_minted*
*.pyg
# morewrites
*.mw
# mylatexformat
*.fmt
# nomencl
*.nlo
# sagetex
*.sagetex.sage
*.sagetex.py
*.sagetex.scmd
# sympy
*.sout
*.sympy
sympy-plots-for-*.tex/
# pdfcomment
*.upa
*.upb
#pythontex
*.pytxcode
pythontex-files-*/
# Texpad
.texpadtmp
# TikZ & PGF
*.dpth
*.md5
*.auxlock
# todonotes
*.tdo
# xindy
*.xdy
# xypic precompiled matrices
*.xyc
# WinEdt
*.bak
*.sav
# endfloat
*.ttt
*.fff
# Latexian
TSWLatexianTemp*
source/main.pdf
.dropbox
================================================
FILE: LICENSE
================================================
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OF ANY KIND, EITHER EXPRESSED OR IMPLIED, INCLUDING, BUT NOT LIMITED TO,
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IS WITH YOU. SHOULD THE PROGRAM PROVE DEFECTIVE, YOU ASSUME THE COST OF
ALL NECESSARY SERVICING, REPAIR OR CORRECTION.
16. Limitation of Liability.
IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING
WILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MODIFIES AND/OR CONVEYS
THE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY
GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE
USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED TO LOSS OF
DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY YOU OR THIRD
PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER PROGRAMS),
EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF
SUCH DAMAGES.
17. Interpretation of Sections 15 and 16.
If the disclaimer of warranty and limitation of liability provided
above cannot be given local legal effect according to their terms,
reviewing courts shall apply local law that most closely approximates
an absolute waiver of all civil liability in connection with the
Program, unless a warranty or assumption of liability accompanies a
copy of the Program in return for a fee.
END OF TERMS AND CONDITIONS
How to Apply These Terms to Your New Programs
If you develop a new program, and you want it to be of the greatest
possible use to the public, the best way to achieve this is to make it
free software which everyone can redistribute and change under these terms.
To do so, attach the following notices to the program. It is safest
to attach them to the start of each source file to most effectively
state the exclusion of warranty; and each file should have at least
the "copyright" line and a pointer to where the full notice is found.
<one line to give the program's name and a brief idea of what it does.>
Copyright (C) <year> <name of author>
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but WITHOUT ANY WARRANTY; without even the implied warranty of
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along with this program. If not, see <http://www.gnu.org/licenses/>.
Also add information on how to contact you by electronic and paper mail.
If the program does terminal interaction, make it output a short
notice like this when it starts in an interactive mode:
<program> Copyright (C) <year> <name of author>
This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'.
This is free software, and you are welcome to redistribute it
under certain conditions; type `show c' for details.
The hypothetical commands `show w' and `show c' should show the appropriate
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You should also get your employer (if you work as a programmer) or school,
if any, to sign a "copyright disclaimer" for the program, if necessary.
For more information on this, and how to apply and follow the GNU GPL, see
<http://www.gnu.org/licenses/>.
The GNU General Public License does not permit incorporating your program
into proprietary programs. If your program is a subroutine library, you
may consider it more useful to permit linking proprietary applications with
the library. If this is what you want to do, use the GNU Lesser General
Public License instead of this License. But first, please read
<http://www.gnu.org/philosophy/why-not-lgpl.html>.
================================================
FILE: README.md
================================================
# Simulator
This simulator serves as the training and evaluation platform in the following work:
> Efficient Large-Scale Fleet Management via Multi-Agent Deep Reinforcement Learning </br>
Kaixiang Lin, Renyu Zhao, Zhe Xu, Jiayu Zhou </br>
[KDD 2018 Long presentation](https://arxiv.org/abs/1802.06444)
### Prerequisites
- Python 2
### Run
```
cd ./tests/
python run_example.py
```
### Docs
Please find more details of usage in [Wiki](https://github.com/illidanlab/Simulator/wiki)
### References
If you find this work helpful in your research, please consider citing the following paper. The bibtex are listed below:
```
@article{lin2018efficient,
title={Efficient Large-Scale Fleet Management via Multi-Agent Deep Reinforcement Learning},
author={Lin, Kaixiang and Zhao, Renyu and Xu, Zhe and Zhou, Jiayu},
journal={arXiv preprint arXiv:1802.06444},
year={2018}
}
```
================================================
FILE: algorithm/IDQN.py
================================================
import numpy as np
import tensorflow as tf
import random, os
from alg_utility import *
class Estimator:
""" build Deep Q network
"""
def __init__(self,
sess,
action_dim,
state_dim,
env,
scope="estimator",
summaries_dir=None):
self.sess = sess
self.n_valid_grid = env.n_valid_grids
self.action_dim = action_dim
self.state_dim = state_dim
self.M = env.M
self.N = env.N
self.scope = scope
self.T = 144
self.env = env
# Writes Tensorboard summaries to disk
self.summary_writer = None
with tf.variable_scope(scope):
# Build the graph
self._build_model()
if summaries_dir:
summary_dir = os.path.join(summaries_dir, "summaries_{}".format(scope))
if not os.path.exists(summary_dir):
os.makedirs(summary_dir)
self.summary_writer = tf.summary.FileWriter(summary_dir)
self.neighbors_list = []
for idx, node_id in enumerate(env.target_grids):
neighbor_indices = env.nodes[node_id].layers_neighbors_id[0] # index in env.nodes
neighbor_ids = [env.target_grids.index(env.nodes[item].get_node_index()) for item in neighbor_indices]
neighbor_ids.append(idx)
# index in env.target_grids == index in state
self.neighbors_list.append(neighbor_ids)
# compute valid action mask.
self.valid_action_mask = np.ones((self.n_valid_grid, self.action_dim))
self.valid_neighbor_node_id = np.zeros((self.n_valid_grid, self.action_dim)) # id in env.nodes
self.valid_neighbor_grid_id = np.zeros((self.n_valid_grid, self.action_dim)) # id in env.target_grids
for grid_idx, grid_id in enumerate(env.target_grids):
for neighbor_idx, neighbor in enumerate(self.env.nodes[grid_id].neighbors):
if neighbor is None:
self.valid_action_mask[grid_idx, neighbor_idx] = 0
else:
node_index = neighbor.get_node_index() # node_index in env.nodes
self.valid_neighbor_node_id[grid_idx, neighbor_idx] = node_index
self.valid_neighbor_grid_id[grid_idx, neighbor_idx] = env.target_grids.index(node_index)
self.valid_neighbor_node_id[grid_idx, -1] = grid_id
self.valid_neighbor_grid_id[grid_idx, -1] = grid_idx
def _build_model(self):
trainable = True
self.state = X = tf.placeholder(shape=[None, self.state_dim], dtype=tf.float32, name="X")
# The TD target value
self.y_pl = tf.placeholder(shape=[None], dtype=tf.float32, name="y")
# action chosen
self.ACTION = tf.placeholder(tf.float32, [None, self.action_dim], 'action_chosen')
self.loss_lr = tf.placeholder(tf.float32, None, "learning_rate")
# 3 layers feed forward network.
l1 = tf.layers.dense(X, 128, tf.nn.elu, trainable=trainable)
l2 = tf.layers.dense(l1, 64, tf.nn.elu, trainable=trainable)
l3 = tf.layers.dense(l2, 32, tf.nn.elu, trainable=trainable)
self.qvalue = tf.layers.dense(l3, self.action_dim, tf.nn.elu, trainable=trainable)
# get the Q(s,a) for chosen action
self.action_predictions = tf.reduce_sum(self.qvalue * self.ACTION, axis=1)
self.losses = tf.squared_difference(self.y_pl, self.action_predictions)
self.loss = tf.reduce_mean(self.losses)
self.train_op = tf.train.AdamOptimizer(self.loss_lr).minimize(self.loss)
# Summaries for Tensorboard
self.summaries = tf.summary.merge([
tf.summary.scalar("loss", self.loss),
tf.summary.histogram("loss_hist", self.losses),
tf.summary.histogram("q_values_hist", self.qvalue),
tf.summary.scalar("max_q_value", tf.reduce_max(self.qvalue))
])
def predict(self, s):
qvalues = self.sess.run(self.qvalue, {self.state: s})
max_qvalue = np.max(qvalues, axis=1)
return max_qvalue
def action(self, s, context, epsilon):
""" Compute Q(s, a) for all actions give states
:return:
"""
# A = np.ones(self.action_dim, dtype=float) * epsilon / self.action_dim
qvalues = self.sess.run(self.qvalue, {self.state: s})
action_idx = [] # go to which node, the index in nodes
action_idx_valid = [] #the index in env.target_grids
action_neighbor_idx = []
action_tuple_mat = np.zeros((len(self.env.nodes), len(self.env.nodes)))
action_tuple = []
action_starting_gridids = []
grid_ids = np.argmax(s[:, -self.n_valid_grid:], axis=1) # starting grid of each sample
valid_probs = []
for idx, grid_valid_idx in enumerate(grid_ids):
curr_qvalue = qvalues[idx]
temp_qvalue = self.valid_action_mask[grid_valid_idx] * curr_qvalue
if np.sum(temp_qvalue) == 0: # encourage exploration
temp_qvalue[self.valid_action_mask[grid_valid_idx]>0] = 1. / np.sum(self.valid_action_mask[grid_valid_idx])
action_prob = temp_qvalue / np.sum(temp_qvalue)
curr_action_indices = np.random.multinomial(int(context[idx]), action_prob)
else:
best_action = np.argmax(temp_qvalue)
action_prob = np.zeros(self.action_dim)
num_valid_action = np.count_nonzero(temp_qvalue)
action_prob[temp_qvalue > 0] = epsilon / float(num_valid_action)
action_prob[best_action] += 1 - epsilon
curr_action_indices = np.random.multinomial(int(context[idx]), action_prob)
valid_probs.append(action_prob)
start_node_id = self.env.target_grids[grid_valid_idx]
num_distinct_action = 0
for curr_action_idx, num_driver in enumerate(curr_action_indices):
if num_driver > 0:
end_node_id = int(self.valid_neighbor_node_id[grid_valid_idx, curr_action_idx])
action_idx.append(end_node_id)
action_idx_valid.append(int(self.valid_neighbor_grid_id[grid_valid_idx, curr_action_idx]))
action_neighbor_idx.append(curr_action_idx)
action_tuple_mat[start_node_id, end_node_id] = num_driver
num_distinct_action += 1
action_starting_gridids.append(num_distinct_action)
action_indices = np.where(action_tuple_mat > 0)
for xx, yy in zip(action_indices[0], action_indices[1]):
if xx != yy:
action_tuple.append((xx, yy, int(action_tuple_mat[xx, yy])))
return qvalues, action_idx, action_idx_valid, action_neighbor_idx, action_tuple, action_starting_gridids
def update(self, s, a, y, learning_rate, global_step):
"""
Updates the estimator towards the given targets.
Args:
s: State input of shape [batch_size, state_dim]
a: Chosen actions of shape [batch_size, action_dim], 0, 1 mask
y: Targets of shape [batch_size]
Returns:
The calculated loss on the batch.
"""
sess = self.sess
feed_dict = {self.state: s, self.y_pl: y, self.ACTION: a, self.loss_lr: learning_rate}
summaries, _, loss = sess.run([self.summaries, self.train_op, self.loss], feed_dict)
if self.summary_writer:
self.summary_writer.add_summary(summaries, global_step)
return loss
class stateProcessor:
"""
Process a raw global state into the states of grids.
"""
def __init__(self,
target_id_states,
target_grids,
n_valid_grids):
self.target_id_states = target_id_states # valid grid index for driver and order distribution.
self.target_grids = target_grids # valid grid id [22, 24, ...] 504
self.n_valid_grids = n_valid_grids
self.T = 144
self.action_dim = 7
self.extend_state = True
def utility_conver_states(self, curr_state):
curr_s = np.array(curr_state).flatten()
curr_s_new = [curr_s[idx] for idx in self.target_id_states]
return np.array(curr_s_new)
def utility_conver_reward(self, reward_node):
reward_node_new = [reward_node[idx] for idx in self.target_grids]
return np.array(reward_node_new)
def reward_wrapper(self, info, curr_s):
"""
:param info: [node_reward(including neighbors), neighbor_reward]
:param curr_s:
:return:
"""
info_reward = info[0]
valid_nodes_reward = self.utility_conver_reward(info_reward[0])
devide = curr_s[:self.n_valid_grids]
devide[devide == 0] = 1
valid_nodes_reward = valid_nodes_reward/devide
return valid_nodes_reward
def compute_context(self, info):
# compute context
context = info.flatten()
context = [context[idx] for idx in self.target_grids]
return context
def utility_normalize_states(self, curr_s):
max_driver_num = np.max(curr_s[:self.n_valid_grids])
max_order_num = np.max(curr_s[self.n_valid_grids:])
curr_s_new = np.zeros_like(curr_s)
curr_s_new[:self.n_valid_grids] = curr_s[:self.n_valid_grids] / max_driver_num
curr_s_new[self.n_valid_grids:] = curr_s[self.n_valid_grids:] / max_order_num
return curr_s_new
def to_grid_states(self, curr_s, curr_city_time):
T = self.T
# curr_s = self.utility_conver_states(curr_state)
time_one_hot = np.zeros((T))
time_one_hot[curr_city_time % T] = 1
onehot_grid_id = np.eye(self.n_valid_grids)
s_grid = np.zeros((self.n_valid_grids, self.n_valid_grids * 3 + T))
s_grid[:, :self.n_valid_grids * 2] = np.stack([curr_s] * self.n_valid_grids)
s_grid[:, self.n_valid_grids * 2:self.n_valid_grids * 2 + T] = np.stack([time_one_hot] * self.n_valid_grids)
s_grid[:, -self.n_valid_grids:] = onehot_grid_id
return np.array(s_grid)
def to_grid_rewards(self, action_idx_valid, node_reward):
r_grid = []
for end_grid_id in action_idx_valid:
r_grid.append(node_reward[end_grid_id])
return np.array(r_grid)
def to_grid_next_states(self, s_grid, next_state, action_index, curr_city_time):
"""
:param s_grid: batch_size x state_dimension
:param action_index: batch_size, end_valid_grid_id, next grid id.
:return:
"""
T = self.T
next_s = self.utility_normalize_states(self.utility_conver_states(next_state))
time_one_hot = np.zeros((T))
time_one_hot[curr_city_time % T] = 1
s_grid_next = np.zeros(s_grid.shape)
s_grid_next[:, :self.n_valid_grids*2] = next_s
s_grid_next[:, self.n_valid_grids*2:self.n_valid_grids*2+T] = time_one_hot
action_index = np.array(action_index) + self.n_valid_grids*2 + T
s_grid_next[np.arange(s_grid_next.shape[0]), action_index] = 1
return s_grid_next
def to_grid_state_for_training(self, s_grid, action_starting_gridids):
s_grid_new = []
for idx, num_extend in enumerate(action_starting_gridids):
temp_s = s_grid[idx]
s_grid_new += [temp_s] * num_extend
return np.array(s_grid_new)
def to_action_mat(self, action_neighbor_idx):
action_mat = np.zeros((len(action_neighbor_idx), self.action_dim))
action_mat[np.arange(action_mat.shape[0]), action_neighbor_idx] = 1
return action_mat
class ReplayMemory:
""" collect the experience and sample a batch for training networks.
without time ordering
"""
def __init__(self, memory_size, batch_size):
self.states = []
self.next_states = []
self.actions = []
self.rewards = []
self.batch_size = batch_size
self.memory_size = memory_size
self.current = 0
self.curr_lens = 0
def add(self, s, a, r, next_s):
if self.curr_lens == 0:
self.states = s
self.actions = a
self.rewards = r
self.next_states = next_s
self.curr_lens = self.states.shape[0]
elif self.curr_lens <= self.memory_size:
self.states = np.concatenate((self.states, s),axis=0)
self.next_states = np.concatenate((self.next_states, next_s), axis=0)
self.actions = np.concatenate((self.actions, a), axis=0)
self.rewards = np.concatenate((self.rewards, r), axis=0)
self.curr_lens = self.states.shape[0]
else:
new_sample_lens = s.shape[0]
index = random.randint(0, self.curr_lens - new_sample_lens)
self.states[index:(index + new_sample_lens)] = s
self.actions[index:(index + new_sample_lens)] = a
self.rewards[index:(index + new_sample_lens)] = r
self.next_states[index:(index + new_sample_lens)] = next_s
def sample(self):
if self.curr_lens <= self.batch_size:
return [self.states, self.actions, self.rewards, self.next_states]
indices = random.sample(range(0, self.curr_lens), self.batch_size)
batch_s = self.states[indices]
batch_a = self.actions[indices]
batch_r = self.rewards[indices]
batch_next_s = self.next_states[indices]
return [batch_s, batch_a, batch_r, batch_next_s]
def reset(self):
self.states = []
self.actions = []
self.rewards = []
self.next_states = []
self.curr_lens = 0
class ModelParametersCopier():
"""
Copy model parameters of one estimator to another.
"""
def __init__(self, estimator1, estimator2):
"""
Defines copy-work operation graph.
Args:
estimator1: Estimator to copy the paramters from
estimator2: Estimator to copy the parameters to
"""
e1_params = [t for t in tf.trainable_variables() if t.name.startswith(estimator1.scope)]
e1_params = sorted(e1_params, key=lambda v: v.name)
e2_params = [t for t in tf.trainable_variables() if t.name.startswith(estimator2.scope)]
e2_params = sorted(e2_params, key=lambda v: v.name)
self.update_ops = []
for e1_v, e2_v in zip(e1_params, e2_params):
op = e2_v.assign(e1_v)
self.update_ops.append(op)
def make(self, sess):
"""
Makes copy.
Args:
sess: Tensorflow session instance
"""
sess.run(self.update_ops)
================================================
FILE: algorithm/__init__.py
================================================
================================================
FILE: algorithm/alg_utility.py
================================================
import tensorflow as tf
import numpy as np
# import cvxpy as cvx
from simulator.utilities import *
""" Some of Following codes are modified from https://github.com/openai/baselines
"""
def tfsum(x, axis=None, keepdims=False):
axis = None if axis is None else [axis]
return tf.reduce_sum(x, axis=axis, keepdims=keepdims)
class Pd(object):
"""
A particular probability distribution
"""
def mode(self):
raise NotImplementedError
def neglogp(self, x):
# Usually it's easier to define the negative logprob
raise NotImplementedError
def kl(self, other):
raise NotImplementedError
def entropy(self):
raise NotImplementedError
def sample(self):
raise NotImplementedError
def logp(self, x):
return - self.neglogp(x)
class DiagGaussianPd(Pd):
def __init__(self, mu, logstd):
self.mean = mu
self.logstd = logstd
self.std = tf.exp(logstd)
def mode(self):
return self.mean
def neglogp(self, x):
# axis = -1, sum over last dimension, first dimension is batch size
return 0.5 * tfsum(tf.square((x - self.mean) / self.std), axis=-1) \
+ 0.5 * np.log(2.0 * np.pi) * tf.to_float(tf.shape(x)[-1]) \
+ tfsum(self.logstd, axis=-1)
def sample(self):
return self.mean + self.std * tf.random_normal(tf.shape(self.mean))
def normalize_reward(discounted_epr):
reward_mean = np.mean(discounted_epr)
reward_std = np.std(discounted_epr)
discounted_epr = (discounted_epr - reward_mean)/reward_std
return discounted_epr
# def projection(Y, n, num_idle_driver):
# assert np.sum(Y) > num_idle_driver
# X = cvx.Variable(n)
# objective = cvx.Minimize(cvx.sum_squares(X - Y))
# constraints = [0 <= X,
# num_idle_driver == cvx.sum_entries(X)]
# prob = cvx.Problem(objective, constraints)
#
# # The optimal objective is returned by prob.solve().
# result = prob.solve()
# return X.value
def continuous_quadratic_knapsack(b, u, r):
"""
OBJECTIVE
min 1/2*||x||_2^2
s.t. b'*x = r, 0<= x <= u, b > 0
Related paper
[1] KC. Kiwiel. On linear-time algorithms for the continuous
quadratic knapsack problem, Journal of Optimization Theory
and Applications, 2007
Coding Reference:
https://github.com/jiayuzhou/MALSAR/blob/master/MALSAR/functions/CMTL/bsa_ihb.m
"""
n = len(b)
break_flag = 0
t_l = np.zeros(n)
t_u = -u # ( 0 - u)/1
t_L = -float('Inf')
t_U = float('Inf')
g_tL = 0
g_tU = 0
T = np.concatenate((t_l, t_u), axis=0)
n_iter = 0
while len(T) !=0:
n_iter += 1
g_t = 0
t_hat = np.median(T)
U_inds = np.where(t_hat < t_u)
M = np.where((t_u <= t_hat) & (t_hat <= t_l))
if len(U_inds[0]) != 0:
g_t = g_t + np.dot(b[U_inds], u[U_inds])
if len(M[0]) != 0:
g_t = g_t - np.dot(b[M], t_hat*b[M]) # a = 0 np.sum(b(M).*(a(M) - t_hat*b(M)))
if g_t > r:
t_L = t_hat
T = T[np.where(T > t_hat)]
g_tL = g_t
elif g_t < r:
t_U = t_hat
T = T[np.where(T < t_hat)]
g_tU = g_t
else:
t_star = t_hat
break_flag = 1
break
if break_flag == 0:
t_star = t_L - (g_tL -r)*(t_U - t_L)/(g_tU - g_tL)
x_star = np.minimum(np.maximum(0, -t_star*b), u)
return x_star
def projection_fast(u, n, num_idle_driver):
b = np.ones((n))
r = np.sum(u) - num_idle_driver
x_star = continuous_quadratic_knapsack(b, u, r)
return u - x_star
def categorical_sample_split(logits, d=6):
"""
:param logits: sampling according to the probability exp(logits)
:param d: first dimension of logits. 6 in our case.
:return:
"""
value = [tf.multinomial(logits[i] - tf.reduce_max(logits[i], [1], keep_dims=True), 1)
for i in np.arange(d)
]
return value
def fc(x, scope, nh, act=tf.nn.relu, init_scale=1.0):
with tf.variable_scope(scope):
nin = x.get_shape()[1].value
# w = tf.get_variable("w", [nin, nh], initializer=ortho_init(init_scale))
w = tf.get_variable("w", [nin, nh], initializer=tf.contrib.layers.xavier_initializer(uniform=True, seed=0))
b = tf.get_variable("b", [nh], initializer=tf.constant_initializer(0.0))
z = tf.matmul(x, w)+b
h = act(z)
return h
def ortho_init(scale=1.0):
def _ortho_init(shape, dtype, partition_info=None):
#lasagne ortho init for tf
shape = tuple(shape)
if len(shape) == 2:
flat_shape = shape
elif len(shape) == 4: # assumes NHWC
flat_shape = (np.prod(shape[:-1]), shape[-1])
else:
raise NotImplementedError
np.random.seed(1)
a = np.random.normal(0.0, 1.0, flat_shape)
u, _, v = np.linalg.svd(a, full_matrices=False)
q = u if u.shape == flat_shape else v # pick the one with the correct shape
q = q.reshape(shape)
return (scale * q[:shape[0], :shape[1]]).astype(np.float32)
return _ortho_init
##############################################################
### utility function for tune DQN
##############################################################
def get_target_ids_local(mapped_matrix_int_local):
row_inds, col_inds = np.where(mapped_matrix_int_local >= 0)
M_local, N_local = mapped_matrix_int_local.shape
target_ids_local = [] # start from 0.
for x, y in zip(row_inds, col_inds):
node_id = ids_2dto1d(x, y, M_local, N_local)
target_ids_local.append(node_id)
return target_ids_local
def collision_action(action_tuple):
count = 0
action_set = set(())
for item in action_tuple:
if item[1] == -1:
continue
grid_id_key = str(item[0]) + "-" + str(item[1])
action_set.add(grid_id_key)
conflict_id_key = str(item[1]) + "-" + str(item[0])
if conflict_id_key in action_set:
count += 1
return count
def construct_grid_nodeid_mapping(target_ids_local, grid_ids_local):
node_mapping = {}
grid_mapping = {} #
for nodeid, gridid in zip(target_ids_local, grid_ids_local):
node_mapping[gridid] = nodeid
grid_mapping[nodeid] = gridid
return node_mapping, grid_mapping
def utility_conver_states(curr_s, target_id_states):
curr_s_new = [curr_s[idx] for idx in target_id_states]
return np.array(curr_s_new)
def utility_conver_reward(reward_node, target_id_states):
reward_node_new = [reward_node[idx] for idx in target_id_states]
return np.array(reward_node_new)
##############################################################
def compute_sum_qtable(temp_qtable):
temp_q = 0
for item in temp_qtable:
for jj in item:
temp_q += np.sum(jj)
return temp_q
================================================
FILE: algorithm/cA2C.py
================================================
import numpy as np
import tensorflow as tf
import random, os
from alg_utility import *
from copy import deepcopy
class Estimator:
""" build value network
"""
def __init__(self,
sess,
action_dim,
state_dim,
env,
scope="estimator",
summaries_dir=None):
self.sess = sess
self.n_valid_grid = env.n_valid_grids
self.action_dim = action_dim
self.state_dim = state_dim
self.M = env.M
self.N = env.N
self.scope = scope
self.T = 144
self.env = env
# Writes Tensorboard summaries to disk
self.summary_writer = None
with tf.variable_scope(scope):
# Build the value function graph
# with tf.variable_scope("value"):
value_loss = self._build_value_model()
with tf.variable_scope("policy"):
actor_loss, entropy = self._build_mlp_policy()
self.loss = actor_loss + .5 * value_loss - 10 * entropy
# self.loss_gradients = tf.gradients(self.value_loss, tf.trainable_variables(scope=scope))
# tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, scope=self.scope))
# Summaries for Tensorboard
self.summaries = tf.summary.merge([
tf.summary.scalar("value_loss", self.value_loss),
tf.summary.scalar("value_output", tf.reduce_mean(self.value_output)),
# tf.summary.scalar("gradient_norm_policy", tf.reduce_sum([tf.norm(item) for item in self.loss_gradients]))
])
self.policy_summaries = tf.summary.merge([
tf.summary.scalar("policy_loss", self.policy_loss),
tf.summary.scalar("adv", tf.reduce_mean(self.tfadv)),
tf.summary.scalar("entropy", self.entropy),
# tf.summary.scalar("gradient_norm_policy", tf.reduce_sum([tf.norm(item) for item in self.loss_gradients]))
])
if summaries_dir:
summary_dir = os.path.join(summaries_dir, "summaries_{}".format(scope))
if not os.path.exists(summary_dir):
os.makedirs(summary_dir)
self.summary_writer = tf.summary.FileWriter(summary_dir)
self.neighbors_list = []
for idx, node_id in enumerate(env.target_grids):
neighbor_indices = env.nodes[node_id].layers_neighbors_id[0] # index in env.nodes
neighbor_ids = [env.target_grids.index(env.nodes[item].get_node_index()) for item in neighbor_indices]
neighbor_ids.append(idx)
# index in env.target_grids == index in state
self.neighbors_list.append(neighbor_ids)
# compute valid action mask.
self.valid_action_mask = np.ones((self.n_valid_grid, self.action_dim))
self.valid_neighbor_node_id = np.zeros((self.n_valid_grid, self.action_dim)) # id in env.nodes
self.valid_neighbor_grid_id = np.zeros((self.n_valid_grid, self.action_dim)) # id in env.target_grids
for grid_idx, grid_id in enumerate(env.target_grids):
for neighbor_idx, neighbor in enumerate(self.env.nodes[grid_id].neighbors):
if neighbor is None:
self.valid_action_mask[grid_idx, neighbor_idx] = 0
else:
node_index = neighbor.get_node_index() # node_index in env.nodes
self.valid_neighbor_node_id[grid_idx, neighbor_idx] = node_index
self.valid_neighbor_grid_id[grid_idx, neighbor_idx] = env.target_grids.index(node_index)
self.valid_neighbor_node_id[grid_idx, -1] = grid_id
self.valid_neighbor_grid_id[grid_idx, -1] = grid_idx
def _build_value_model(self):
self.state = X = tf.placeholder(shape=[None, self.state_dim], dtype=tf.float32, name="X")
# The TD target value
self.y_pl = tf.placeholder(shape=[None, 1], dtype=tf.float32, name="y")
self.loss_lr = tf.placeholder(tf.float32, None, "learning_rate")
# 3 layers feed forward network.
l1 = fc(X, "l1", 128, act=tf.nn.relu)
l2 = fc(l1, "l2", 64, act=tf.nn.relu)
l3 = fc(l2, "l3", 32, act=tf.nn.relu)
# l1 = tf.layers.dense(X, 1024, tf.nn.sigmoid, trainable=trainable)
# l2 = tf.layers.dense(l1, 512, tf.nn.sigmoid, trainable=trainable)
# l3 = tf.layers.dense(l2, 32, tf.nn.sigmoid, trainable=trainable)
self.value_output = fc(l3, "value_output", 1, act=tf.nn.relu)
# self.losses = tf.square(self.y_pl - self.value_output)
self.value_loss = tf.reduce_mean(tf.squared_difference(self.y_pl, self.value_output))
self.value_train_op = tf.train.AdamOptimizer(self.loss_lr).minimize(self.value_loss)
return self.value_loss
def _build_mlp_policy(self):
self.policy_state = tf.placeholder(shape=[None, self.state_dim], dtype=tf.float32, name="P")
self.ACTION = tf.placeholder(shape=[None, self.action_dim], dtype=tf.float32, name="action")
self.tfadv = tf.placeholder(shape=[None, 1], dtype=tf.float32, name='advantage')
self.neighbor_mask = tf.placeholder(shape=[None, self.action_dim], dtype=tf.float32, name="neighbormask")
# this mask filter invalid actions and those action smaller than current grid value.
l1 = fc(self.policy_state, "l1", 128, act=tf.nn.relu)
l2 = fc(l1, "l2", 64, act=tf.nn.relu)
l3 = fc(l2, "l3", 32, act=tf.nn.relu)
self.logits = logits = fc(l3, "logits", self.action_dim, act=tf.nn.relu) + 1 # avoid valid_logits are all zeros
self.valid_logits = logits * self.neighbor_mask
self.softmaxprob = tf.nn.softmax(tf.log(self.valid_logits + 1e-8))
self.logsoftmaxprob = tf.nn.log_softmax(self.softmaxprob)
self.neglogprob = - self.logsoftmaxprob * self.ACTION
self.actor_loss = tf.reduce_mean(tf.reduce_sum(self.neglogprob * self.tfadv, axis=1))
self.entropy = - tf.reduce_mean(self.softmaxprob * self.logsoftmaxprob)
self.policy_loss = self.actor_loss - 0.01 * self.entropy
self.policy_train_op = tf.train.AdamOptimizer(self.loss_lr).minimize(self.policy_loss)
return self.actor_loss, self.entropy
def predict(self, s):
value_output = self.sess.run(self.value_output, {self.state: s})
return value_output
def action(self, s, context, epsilon):
""" Compute current action for all grids give states
:param s: 504 x stat_dim,
:return:
"""
value_output = self.sess.run(self.value_output, {self.state: s}).flatten()
action_tuple = []
valid_prob = []
# for training policy gradient.
action_choosen_mat = []
policy_state = []
curr_state_value = []
next_state_ids = []
grid_ids = np.argmax(s[:, -self.n_valid_grid:], axis=1)
# compute neighbor mask according to centralized value
curr_neighbor_mask = deepcopy(self.valid_action_mask)
for idx, grid_valid_idx in enumerate(grid_ids):
valid_qvalues = value_output[self.neighbors_list[grid_valid_idx]] # value of current and its nearby grids
temp_qvalue = np.zeros(self.action_dim)
temp_qvalue[curr_neighbor_mask[grid_valid_idx] > 0] = valid_qvalues
temp_qvalue[temp_qvalue < temp_qvalue[-1]] = 0
curr_neighbor_mask[grid_valid_idx][np.where(temp_qvalue < temp_qvalue[-1])] = 0
if np.sum(curr_neighbor_mask[grid_valid_idx]) == 0:
curr_neighbor_mask[grid_valid_idx] = self.valid_action_mask[grid_valid_idx]
# compute policy probability.
action_probs = self.sess.run(self.softmaxprob, {self.policy_state: s,
self.neighbor_mask: curr_neighbor_mask})
curr_neighbor_mask_policy = []
# sample action.
for idx, grid_valid_idx in enumerate(grid_ids):
action_prob = action_probs[idx]
# cast invalid action to zero, avlid numerical issue.
action_prob[self.valid_action_mask[grid_valid_idx] == 0] = 0
valid_prob.append(action_prob) # action probability for state value function
if int(context[idx]) == 0:
continue
curr_action_indices_temp = np.random.choice(self.action_dim, int(context[idx]),
p=action_prob/np.sum(action_prob))
# num of drivers dispatched to nearby locations [2,3,2,3,1,3,3]
# for numerically stable, avoid sum of action_prob > 1 with small value
curr_action_indices = [0] * self.action_dim
for kk in curr_action_indices_temp:
curr_action_indices[kk] += 1
start_node_id = self.env.target_grids[grid_valid_idx]
for curr_action_idx, num_driver in enumerate(curr_action_indices):
if num_driver > 0:
end_node_id = int(self.valid_neighbor_node_id[grid_valid_idx, curr_action_idx])
if end_node_id != start_node_id:
action_tuple.append((start_node_id, end_node_id, num_driver))
# book keeping for training
temp_a = np.zeros(self.action_dim)
temp_a[curr_action_idx] = 1
action_choosen_mat.append(temp_a)
policy_state.append(s[idx])
curr_state_value.append(value_output[idx])
next_state_ids.append(self.valid_neighbor_grid_id[grid_valid_idx, curr_action_idx])
curr_neighbor_mask_policy.append(curr_neighbor_mask[idx])
return action_tuple, np.stack(valid_prob), \
np.stack(policy_state), np.stack(action_choosen_mat), curr_state_value, \
np.stack(curr_neighbor_mask_policy), next_state_ids
def compute_advantage(self, curr_state_value, next_state_ids, next_state, node_reward, gamma):
"""for policy network"""
advantage = []
node_reward = node_reward.flatten()
qvalue_next = self.sess.run(self.value_output, {self.state: next_state}).flatten()
for idx, next_state_id in enumerate(next_state_ids):
next_state_id = int(next_state_id)
temp_adv = node_reward[next_state_id] + gamma * qvalue_next[next_state_id] - curr_state_value[idx]
advantage.append(temp_adv)
return advantage
def compute_targets(self, valid_prob, next_state, node_reward, gamma):
targets = []
node_reward = node_reward.flatten()
qvalue_next = self.sess.run(self.value_output, {self.state: next_state}).flatten()
for idx in np.arange(self.n_valid_grid):
grid_prob = valid_prob[idx][self.valid_action_mask[idx]>0]
neighbor_grid_ids = self.neighbors_list[idx]
curr_grid_target = np.sum(grid_prob * (node_reward[neighbor_grid_ids] + gamma * qvalue_next[neighbor_grid_ids]))
# assert np.sum(grid_prob) == 1 numerical issue.
targets.append(curr_grid_target)
return np.array(targets).reshape([-1, 1])
def initialization(self, s, y, learning_rate):
sess = self.sess
feed_dict = {self.state: s, self.y_pl: y, self.loss_lr: learning_rate}
_, value_loss = sess.run([self.value_train_op, self.value_loss], feed_dict)
return value_loss
def update_policy(self, policy_state, advantage, action_choosen_mat, curr_neighbor_mask, learning_rate, global_step):
sess = self.sess
feed_dict = {self.policy_state: policy_state,
self.tfadv: advantage,
self.ACTION: action_choosen_mat,
self.neighbor_mask: curr_neighbor_mask,
self.loss_lr: learning_rate}
summaries, _, loss = sess.run([self.policy_summaries, self.policy_train_op, self.policy_loss], feed_dict)
if self.summary_writer:
self.summary_writer.add_summary(summaries, global_step)
self.summary_writer.flush()
return loss
def update_value(self, s, y, learning_rate, global_step):
"""
Updates the estimator towards the given targets.
Args:
s: State input of shape [batch_size, state_dim]
a: Chosen actions of shape [batch_size, action_dim], 0, 1 mask
y: Targets of shape [batch_size]
Returns:
The calculated loss on the batch.
"""
sess = self.sess
feed_dict = {self.state: s, self.y_pl: y, self.loss_lr: learning_rate}
summaries, _, loss = sess.run([self.summaries, self.value_train_op, self.value_loss], feed_dict)
if self.summary_writer:
self.summary_writer.add_summary(summaries, global_step)
self.summary_writer.flush()
return loss
class stateProcessor:
"""
Process a raw global state into the states of grids.
"""
def __init__(self,
target_id_states,
target_grids,
n_valid_grids):
self.target_id_states = target_id_states # valid grid index for driver and order distribution.
self.target_grids = target_grids # valid grid id [22, 24, ...] 504 grids
self.n_valid_grids = n_valid_grids
self.T = 144
self.action_dim = 7
self.extend_state = True
def utility_conver_states(self, curr_state):
curr_s = np.array(curr_state).flatten()
curr_s_new = [curr_s[idx] for idx in self.target_id_states]
return np.array(curr_s_new)
def utility_normalize_states(self, curr_s):
max_driver_num = np.max(curr_s[:self.n_valid_grids])
max_order_num = np.max(curr_s[self.n_valid_grids:])
if max_order_num == 0:
max_order_num = 1
if max_driver_num == 0:
max_driver_num = 1
curr_s_new = np.zeros_like(curr_s)
curr_s_new[:self.n_valid_grids] = curr_s[:self.n_valid_grids] / max_driver_num
curr_s_new[self.n_valid_grids:] = curr_s[self.n_valid_grids:] / max_order_num
return curr_s_new
def utility_conver_reward(self, reward_node):
reward_node_new = [reward_node[idx] for idx in self.target_grids]
return np.array(reward_node_new)
def reward_wrapper(self, info, curr_s):
""" reformat reward from env to the input of model.
:param info: [node_reward(including neighbors), neighbor_reward]
:param curr_s: processed by utility_conver_states, same time step as info.
:return:
"""
info_reward = info[0]
valid_nodes_reward = self.utility_conver_reward(info_reward[0])
devide = curr_s[:self.n_valid_grids]
devide[devide == 0] = 1
valid_nodes_reward = valid_nodes_reward/devide # averaged rewards for drivers arriving this grid
return valid_nodes_reward
def compute_context(self, info):
# compute context
context = info.flatten()
context = [context[idx] for idx in self.target_grids]
return context
def to_grid_states(self, curr_s, curr_city_time):
""" extend global state to all agents' state.
:param curr_s:
:param curr_city_time: curr_s time step
:return:
"""
T = self.T
# curr_s = self.utility_conver_states(curr_state)
time_one_hot = np.zeros((T))
time_one_hot[curr_city_time % T] = 1
onehot_grid_id = np.eye(self.n_valid_grids)
s_grid = np.zeros((self.n_valid_grids, self.n_valid_grids * 3 + T))
s_grid[:, :self.n_valid_grids * 2] = np.stack([curr_s] * self.n_valid_grids)
s_grid[:, self.n_valid_grids * 2:self.n_valid_grids * 2 + T] = np.stack([time_one_hot] * self.n_valid_grids)
s_grid[:, -self.n_valid_grids:] = onehot_grid_id
return np.array(s_grid)
def to_grid_rewards(self, node_reward):
"""
:param node_reward: curr_city_time + 1 's reward
:return:
"""
return np.array(node_reward).reshape([-1, 1])
def to_action_mat(self, action_neighbor_idx):
action_mat = np.zeros((len(action_neighbor_idx), self.action_dim))
action_mat[np.arange(action_mat.shape[0]), action_neighbor_idx] = 1
return action_mat
class policyReplayMemory:
def __init__(self, memory_size, batch_size):
self.states = []
# self.next_states = []
self.neighbor_mask = []
self.actions = []
self.rewards = [] # advantages
self.batch_size = batch_size
self.memory_size = memory_size
self.current = 0
self.curr_lens = 0
def add(self, s, a, r, mask):
if self.curr_lens == 0:
self.states = s
self.actions = a
self.rewards = r
self.neighbor_mask = mask
self.curr_lens = self.states.shape[0]
elif self.curr_lens <= self.memory_size:
self.states = np.concatenate((self.states, s),axis=0)
self.neighbor_mask = np.concatenate((self.neighbor_mask, mask), axis=0)
self.actions = np.concatenate((self.actions, a), axis=0)
self.rewards = np.concatenate((self.rewards, r), axis=0)
self.curr_lens = self.states.shape[0]
else:
new_sample_lens = s.shape[0]
# random.seed(0)
index = random.randint(0, self.curr_lens - new_sample_lens)
self.states[index:(index + new_sample_lens)] = s
self.actions[index:(index + new_sample_lens)] = a
self.rewards[index:(index + new_sample_lens)] = r
self.neighbor_mask[index:(index + new_sample_lens)] = mask
def sample(self):
if self.curr_lens <= self.batch_size:
return [self.states, self.actions, np.array(self.rewards), self.neighbor_mask]
# random.seed(0)
indices = random.sample(range(0, self.curr_lens), self.batch_size)
batch_s = self.states[indices]
batch_a = self.actions[indices]
batch_r = self.rewards[indices]
batch_mask = self.neighbor_mask[indices]
return [batch_s, batch_a, batch_r, batch_mask]
def reset(self):
self.states = []
self.actions = []
self.rewards = []
self.neighbor_mask = []
self.curr_lens = 0
class ReplayMemory:
""" collect the experience and sample a batch for training networks.
without time ordering
"""
def __init__(self, memory_size, batch_size):
self.states = []
self.next_states = []
self.actions = []
self.rewards = []
self.batch_size = batch_size
self.memory_size = memory_size
self.current = 0
self.curr_lens = 0 # current memory lens
def add(self, s, a, r, next_s):
if self.curr_lens == 0:
self.states = s
self.actions = a
self.rewards = r
self.next_states = next_s
self.curr_lens = self.states.shape[0]
elif self.curr_lens <= self.memory_size:
self.states = np.concatenate((self.states, s),axis=0)
self.next_states = np.concatenate((self.next_states, next_s), axis=0)
self.actions = np.concatenate((self.actions, a), axis=0)
self.rewards = np.concatenate((self.rewards, r), axis=0)
self.curr_lens = self.states.shape[0]
else:
new_sample_lens = s.shape[0]
# random.seed(0)
index = random.randint(0, self.curr_lens - new_sample_lens)
self.states[index:(index + new_sample_lens)] = s
self.actions[index:(index + new_sample_lens)] = a
self.rewards[index:(index + new_sample_lens)] = r
self.next_states[index:(index + new_sample_lens)] = next_s
def sample(self):
if self.curr_lens <= self.batch_size:
return [self.states, self.actions, self.rewards, self.next_states]
# random.seed(0)
indices = random.sample(range(0, self.curr_lens), self.batch_size)
batch_s = self.states[indices]
batch_a = self.actions[indices]
batch_r = self.rewards[indices]
batch_mask = self.next_states[indices]
return [batch_s, batch_a, batch_r, batch_mask]
def reset(self):
self.states = []
self.actions = []
self.rewards = []
self.next_states = []
self.curr_lens = 0
class ModelParametersCopier():
"""
Copy model parameters of one estimator to another.
"""
def __init__(self, estimator1, estimator2):
"""
Defines copy-work operation graph.
Args:
estimator1: Estimator to copy the paramters from
estimator2: Estimator to copy the parameters to
"""
e1_params = [t for t in tf.trainable_variables() if t.name.startswith(estimator1.scope)]
e1_params = sorted(e1_params, key=lambda v: v.name)
e2_params = [t for t in tf.trainable_variables() if t.name.startswith(estimator2.scope)]
e2_params = sorted(e2_params, key=lambda v: v.name)
self.update_ops = []
for e1_v, e2_v in zip(e1_params, e2_params):
op = e2_v.assign(e1_v)
self.update_ops.append(op)
def make(self, sess):
"""
Makes copy.
Args:
sess: Tensorflow session instance
"""
sess.run(self.update_ops)
================================================
FILE: algorithm/cDQN.py
================================================
import numpy as np
import tensorflow as tf
import random, os
from alg_utility import *
# this is essentially deep expected SARSA.
class Estimator:
""" build value network
"""
def __init__(self,
sess,
action_dim,
state_dim,
env,
scope="estimator",
summaries_dir=None):
self.sess = sess
self.n_valid_grid = env.n_valid_grids
self.action_dim = action_dim
self.state_dim = state_dim
self.M = env.M
self.N = env.N
self.scope = scope
self.T = 144
self.env = env
# Writes Tensorboard summaries to disk
self.summary_writer = None
with tf.variable_scope(scope):
# Build the graph
self._build_model()
self.loss_gradients = tf.gradients(self.loss, tf.trainable_variables(scope=scope))
# tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, scope=self.scope))
# Summaries for Tensorboard
self.summaries = tf.summary.merge([
tf.summary.scalar("loss", self.loss),
tf.summary.scalar("value_output", tf.reduce_mean(self.value_output)),
tf.summary.scalar("gradient_norm_policy", tf.reduce_sum([tf.norm(item) for item in self.loss_gradients]))
])
if summaries_dir:
summary_dir = os.path.join(summaries_dir, "summaries_{}".format(scope))
if not os.path.exists(summary_dir):
os.makedirs(summary_dir)
self.summary_writer = tf.summary.FileWriter(summary_dir)
self.neighbors_list = []
for idx, node_id in enumerate(env.target_grids):
neighbor_indices = env.nodes[node_id].layers_neighbors_id[0] # index in env.nodes
neighbor_ids = [env.target_grids.index(env.nodes[item].get_node_index()) for item in neighbor_indices]
neighbor_ids.append(idx)
# index in env.target_grids == index in state
self.neighbors_list.append(neighbor_ids)
# compute valid action mask.
self.valid_action_mask = np.ones((self.n_valid_grid, self.action_dim))
self.valid_neighbor_node_id = np.zeros((self.n_valid_grid, self.action_dim)) # id in env.nodes
self.valid_neighbor_grid_id = np.zeros((self.n_valid_grid, self.action_dim)) # id in env.target_grids
for grid_idx, grid_id in enumerate(env.target_grids):
for neighbor_idx, neighbor in enumerate(self.env.nodes[grid_id].neighbors):
if neighbor is None:
self.valid_action_mask[grid_idx, neighbor_idx] = 0
else:
node_index = neighbor.get_node_index() # node_index in env.nodes
self.valid_neighbor_node_id[grid_idx, neighbor_idx] = node_index
self.valid_neighbor_grid_id[grid_idx, neighbor_idx] = env.target_grids.index(node_index)
self.valid_neighbor_node_id[grid_idx, -1] = grid_id
self.valid_neighbor_grid_id[grid_idx, -1] = grid_idx
def _build_model(self):
trainable = True
self.state = X = tf.placeholder(shape=[None, self.state_dim], dtype=tf.float32, name="X")
# The TD target value
self.y_pl = tf.placeholder(shape=[None, 1], dtype=tf.float32, name="y")
self.loss_lr = tf.placeholder(tf.float32, None, "learning_rate")
# 3 layers feed forward network.
l1 = fc(X, "l1", 128, act=tf.nn.relu)
l2 = fc(l1, "l2", 64, act=tf.nn.relu)
l3 = fc(l2, "l3", 32, act=tf.nn.relu)
# l1 = tf.layers.dense(X, 1024, tf.nn.sigmoid, trainable=trainable)
# l2 = tf.layers.dense(l1, 512, tf.nn.sigmoid, trainable=trainable)
# l3 = tf.layers.dense(l2, 32, tf.nn.sigmoid, trainable=trainable)
self.value_output = fc(l3, "value_output", 1, act=tf.nn.relu)
# self.losses = tf.square(self.y_pl - self.value_output)
self.loss = tf.reduce_mean(tf.squared_difference(self.y_pl, self.value_output))
self.train_op = tf.train.AdamOptimizer(self.loss_lr).minimize(self.loss)
def predict(self, s):
value_output = self.sess.run(self.value_output, {self.state: s})
return value_output
def action(self, s, context, epsilon):
""" Compute current action for all grids give states
:param s: 504 x stat_dim,
:return:
"""
# value of each grid at next time step, dispatched according to this value.
value_output = self.sess.run(self.value_output, {self.state: s}).flatten()
action_tuple = []
valid_prob = []
grid_ids = np.argmax(s[:, -self.n_valid_grid:], axis=1)
for idx, grid_valid_idx in enumerate(grid_ids):
valid_qvalues = value_output[self.neighbors_list[grid_valid_idx]]
temp_qvalue = np.zeros(self.action_dim)
if np.sum(valid_qvalues) == 0:
# all value equals to 0. this could explores conflicts action.
temp_qvalue[self.valid_action_mask[grid_valid_idx] > 0] = 1. / np.sum(
self.valid_action_mask[grid_valid_idx])
action_prob = temp_qvalue
valid_prob.append(action_prob)
else:
temp_qvalue[self.valid_action_mask[grid_valid_idx] > 0] = valid_qvalues
temp_qvalue[temp_qvalue < temp_qvalue[-1]] = 0
best_action = np.argmax(temp_qvalue)
num_valid_action = np.count_nonzero(temp_qvalue)
action_prob = np.zeros_like(temp_qvalue)
action_prob[temp_qvalue > 0] = epsilon / float(num_valid_action)
action_prob[best_action] += 1 - epsilon
valid_prob.append(action_prob)
if int(context[idx]) == 0:
continue
curr_action_indices = np.random.multinomial(int(context[idx]),
action_prob)
start_node_id = self.env.target_grids[grid_valid_idx]
for curr_action_idx, num_driver in enumerate(curr_action_indices):
if num_driver > 0:
end_node_id = int(self.valid_neighbor_node_id[grid_valid_idx, curr_action_idx])
if end_node_id != start_node_id:
action_tuple.append((start_node_id, end_node_id, num_driver))
return action_tuple, np.stack(valid_prob)
def compute_targets(self, valid_prob, next_state, node_reward, gamma):
targets = []
node_reward = node_reward.flatten()
qvalue_next = self.sess.run(self.value_output, {self.state: next_state}).flatten() # qvalue of next state
for idx in np.arange(self.n_valid_grid):
grid_prob = valid_prob[idx][self.valid_action_mask[idx]>0]
neighbor_grid_ids = self.neighbors_list[idx]
best_grid = np.argmax(grid_prob)
curr_grid_target = node_reward[neighbor_grid_ids][best_grid] + gamma * qvalue_next[neighbor_grid_ids][best_grid]
targets.append(curr_grid_target)
return np.array(targets).reshape([-1, 1])
def initialization(self, s, y, learning_rate):
sess = self.sess
feed_dict = {self.state: s, self.y_pl: y, self.loss_lr: learning_rate}
_, loss = sess.run([self.train_op, self.loss], feed_dict)
return loss
def update(self, s, y, learning_rate, global_step):
"""
Updates the estimator towards the given targets.
Args:
s: State input of shape [batch_size, state_dim]
a: Chosen actions of shape [batch_size, action_dim], 0, 1 mask
y: Targets of shape [batch_size]
Returns:
The calculated loss on the batch.
"""
sess = self.sess
feed_dict = {self.state: s, self.y_pl: y, self.loss_lr: learning_rate}
summaries, _, loss = sess.run([self.summaries, self.train_op, self.loss], feed_dict)
if self.summary_writer:
self.summary_writer.add_summary(summaries, global_step)
self.summary_writer.flush()
return loss
def _build_cnn_model(self):
# states of grid id and time
self.state_spacetime = Xst = tf.placeholder(shape=[None, self.n_valid_grid + self.T], dtype=tf.uint8, name="Xst")
# states of distribution
self.state = X = tf.placeholder(shape=[None, self.M, self.N, 4], dtype=tf.uint8, name="X")
# The TD target value
self.y_pl = tf.placeholder(shape=[None], dtype=tf.float32, name="y")
batch_size = tf.shape(self.state)[0]
conv1 = tf.contrib.layers.conv2d(
X, 32, 8, 4, activation_fn=tf.nn.relu)
conv2 = tf.contrib.layers.conv2d(
conv1, 64, 4, 2, activation_fn=tf.nn.relu)
conv3 = tf.contrib.layers.conv2d(
conv2, 64, 3, 1, activation_fn=tf.nn.relu)
# Fully connected layers
flattened = tf.contrib.layers.flatten(conv3)
fc1 = tf.contrib.layers.fully_connected(flattened, 512)
self.predictions = tf.contrib.layers.fully_connected(fc1, self.action_dim)
class stateProcessor:
"""
Process a raw global state into the states of grids.
"""
def __init__(self,
target_id_states,
target_grids,
n_valid_grids):
self.target_id_states = target_id_states # valid grid index for driver and order distribution.
self.target_grids = target_grids # valid grid id [22, 24, ...]
self.n_valid_grids = n_valid_grids
self.T = 144
self.action_dim = 7
self.extend_state = True
def utility_conver_states(self, curr_state):
curr_s = np.array(curr_state).flatten()
curr_s_new = [curr_s[idx] for idx in self.target_id_states]
return np.array(curr_s_new)
def utility_normalize_states(self, curr_s):
max_driver_num = np.max(curr_s[:self.n_valid_grids])
max_order_num = np.max(curr_s[self.n_valid_grids:])
curr_s_new = np.zeros_like(curr_s)
curr_s_new[:self.n_valid_grids] = curr_s[:self.n_valid_grids] / max_driver_num
curr_s_new[self.n_valid_grids:] = curr_s[self.n_valid_grids:] / max_order_num
return curr_s_new
def utility_conver_reward(self, reward_node):
reward_node_new = [reward_node[idx] for idx in self.target_grids]
return np.array(reward_node_new)
def reward_wrapper(self, info, curr_s):
info_reward = info[0]
valid_nodes_reward = self.utility_conver_reward(info_reward[0])
devide = curr_s[:self.n_valid_grids]
devide[devide == 0] = 1
valid_nodes_reward = valid_nodes_reward/devide
return valid_nodes_reward
def compute_context(self, info):
# 计算context
context = info.flatten()
context = [context[idx] for idx in self.target_grids]
return context
def to_grid_states(self, curr_s, curr_city_time):
T = self.T
# curr_s = self.utility_conver_states(curr_state)
time_one_hot = np.zeros((T))
time_one_hot[curr_city_time % T] = 1
onehot_grid_id = np.eye(self.n_valid_grids)
s_grid = np.zeros((self.n_valid_grids, self.n_valid_grids * 3 + T))
s_grid[:, :self.n_valid_grids * 2] = np.stack([curr_s] * self.n_valid_grids)
s_grid[:, self.n_valid_grids * 2:self.n_valid_grids * 2 + T] = np.stack([time_one_hot] * self.n_valid_grids)
s_grid[:, -self.n_valid_grids:] = onehot_grid_id
return np.array(s_grid)
def to_grid_rewards(self, node_reward):
return np.array(node_reward).reshape([-1, 1])
def to_action_mat(self, action_neighbor_idx):
action_mat = np.zeros((len(action_neighbor_idx), self.action_dim))
action_mat[np.arange(action_mat.shape[0]), action_neighbor_idx] = 1
return action_mat
class ReplayMemory:
""" collect the experience and sample a batch for training networks.
without time ordering
"""
def __init__(self, memory_size, batch_size):
self.states = []
self.next_states = []
self.actions = []
self.rewards = []
self.batch_size = batch_size
self.memory_size = memory_size
self.current = 0
self.curr_lens = 0
def add(self, s, a, r, next_s):
if self.curr_lens == 0:
self.states = s
self.actions = a
self.rewards = r
self.next_states = next_s
self.curr_lens = self.states.shape[0]
elif self.curr_lens <= self.memory_size:
self.states = np.concatenate((self.states, s),axis=0)
self.next_states = np.concatenate((self.next_states, next_s), axis=0)
self.actions = np.concatenate((self.actions, a), axis=0)
self.rewards = np.concatenate((self.rewards, r), axis=0)
self.curr_lens = self.states.shape[0]
else:
new_sample_lens = s.shape[0]
index = random.randint(0, self.curr_lens - new_sample_lens)
self.states[index:(index + new_sample_lens)] = s
self.actions[index:(index + new_sample_lens)] = a
self.rewards[index:(index + new_sample_lens)] = r
self.next_states[index:(index + new_sample_lens)] = next_s
def sample(self):
if self.curr_lens <= self.batch_size:
return [self.states, self.actions, self.rewards, self.next_states]
indices = random.sample(range(0, self.curr_lens), self.batch_size)
batch_s = self.states[indices]
batch_a = self.actions[indices]
batch_r = self.rewards[indices]
batch_next_s = self.next_states[indices]
return [batch_s, batch_a, batch_r, batch_next_s]
def reset(self):
self.states = []
self.actions = []
self.rewards = []
self.next_states = []
self.curr_lens = 0
class ModelParametersCopier():
"""
Copy model parameters of one estimator to another.
"""
def __init__(self, estimator1, estimator2):
"""
Defines copy-work operation graph.
Args:
estimator1: Estimator to copy the paramters from
estimator2: Estimator to copy the parameters to
"""
e1_params = [t for t in tf.trainable_variables() if t.name.startswith(estimator1.scope)]
e1_params = sorted(e1_params, key=lambda v: v.name)
e2_params = [t for t in tf.trainable_variables() if t.name.startswith(estimator2.scope)]
e2_params = sorted(e2_params, key=lambda v: v.name)
self.update_ops = []
for e1_v, e2_v in zip(e1_params, e2_params):
op = e2_v.assign(e1_v)
self.update_ops.append(op)
def make(self, sess):
"""
Makes copy.
Args:
sess: Tensorflow session instance
"""
sess.run(self.update_ops)
================================================
FILE: run/run_IDQN.py
================================================
import pickle, sys
sys.path.append("../")
# from simulator.utilities import *
from algorithm.alg_utility import *
from simulator.envs import *
################## Load data ###################################
dir_prefix = "/mnt/research/linkaixi/AllData/dispatch/"
current_time = time.strftime("%Y%m%d_%H-%M")
log_dir = dir_prefix + "dispatch_simulator/experiments/{}/".format(current_time)
mkdir_p(log_dir)
print "log dir is {}".format(log_dir)
data_dir = dir_prefix + "dispatch_realdata/data_for_simulator/"
order_time_dist = []
order_price_dist = []
mapped_matrix_int = pickle.load(open(data_dir+"mapped_matrix_int.pkl", 'rb'))
order_num_dist = pickle.load(open(data_dir+"order_num_dist", 'rb'))
idle_driver_dist_time = pickle.load(open(data_dir+"idle_driver_dist_time", 'rb'))
idle_driver_location_mat = pickle.load(open(data_dir+"idle_driver_location_mat", 'rb'))
target_ids = pickle.load(open(data_dir+"target_grid_id.pkl", 'rb'))
onoff_driver_location_mat = pickle.load(open(data_dir + "onoff_driver_location_mat", 'rb'))
order_filename = dir_prefix + "dispatch_realdata/orders/all_orders_target"
order_real = pickle.load(open(order_filename, 'rb'))
M, N = mapped_matrix_int.shape
print "finish load data"
################## Initialize env ###################################
n_side = 6
GAMMA = 0.9
l_max = 9
env = CityReal(mapped_matrix_int, order_num_dist,
idle_driver_dist_time, idle_driver_location_mat,
order_time_dist, order_price_dist,
l_max, M, N, n_side, 1/28.0, order_real, onoff_driver_location_mat)
log_dir = dir_prefix + "dispatch_simulator/experiments/{}/".format(current_time)
temp = np.array(env.target_grids) + env.M * env.N
target_id_states = env.target_grids + temp.tolist()
# curr_s = np.array(env.reset_clean()).flatten() # [0] driver dist; [1] order dist
# curr_s = utility_conver_states(curr_s, target_id_states)
print "******************* Finish generating one day order **********************"
print "******************* Starting training Deep SARSA **********************"
from algorithm.IDQN import *
MAX_ITER = 50 # 10 iteration the Q-learning loss will converge.
is_plot_figure = False
city_time_start = 0
EP_LEN = 144
global_step = 0
city_time_end = city_time_start + EP_LEN
EPSILON = 0.9
gamma = 0.9
learning_rate = 1e-2
prev_epsiode_reward = 0
all_rewards = []
order_response_rates = []
value_table_sum = []
episode_rewards = []
episode_conflicts_drivers = []
order_response_rate_episode = []
episode_dispatched_drivers = []
T = 144
action_dim = 7
state_dim = env.n_valid_grids * 3 + T
# tf.reset_default_graph()
sess = tf.Session()
tf.set_random_seed(1)
q_estimator = Estimator(sess, action_dim,
state_dim,
env,
scope="q_estimator",
summaries_dir=log_dir)
target_estimator = Estimator(sess, action_dim, state_dim, env, scope="target_q")
sess.run(tf.global_variables_initializer())
estimator_copy = ModelParametersCopier(q_estimator, target_estimator)
replay = ReplayMemory(memory_size=1e+6, batch_size=3000)
stateprocessor = stateProcessor(target_id_states, env.target_grids, env.n_valid_grids)
saver = tf.train.Saver()
save_random_seed = []
N_ITER_RUNS = 25
temp_value = 10
RATIO = 1
EPSILON_start = 0.5
EPSILON_end = 0.1
epsilon_decay_steps=15
epsilons = np.linspace(EPSILON_start, EPSILON_end, epsilon_decay_steps)
for n_iter in np.arange(25):
RANDOM_SEED = n_iter + MAX_ITER - temp_value
env.reset_randomseed(RANDOM_SEED)
save_random_seed.append(RANDOM_SEED)
batch_s, batch_a, batch_r = [], [], []
batch_reward_gmv = []
epsiode_reward = 0
num_dispatched_drivers = 0
# reset env
is_regenerate_order = 1
curr_state = env.reset_clean(generate_order=is_regenerate_order, ratio=RATIO, city_time=city_time_start)
info = env.step_pre_order_assigin(curr_state)
context = stateprocessor.compute_context(info)
curr_s = stateprocessor.utility_conver_states(curr_state)
normalized_curr_s = stateprocessor.utility_normalize_states(curr_s)
s_grid = stateprocessor.to_grid_states(normalized_curr_s, env.city_time) # t0, s0
# record rewards to update the value table
episodes_immediate_rewards = []
num_conflicts_drivers = []
curr_num_actions = []
epsilon = epsilons[n_iter] if n_iter < 15 else EPSILON_end
for ii in np.arange(EP_LEN + 1):
# INPUT: state, OUTPUT: action
qvalues, action_idx, action_idx_valid, action_neighbor_idx, \
action_tuple, action_starting_gridids = q_estimator.action(s_grid, context, epsilon)
# a0
# ONE STEP: r0
next_state, r, info = env.step(action_tuple, 2)
# r0
immediate_reward = stateprocessor.reward_wrapper(info, curr_s)
# a0
action_mat = stateprocessor.to_action_mat(action_neighbor_idx)
# s0
s_grid_train = stateprocessor.to_grid_state_for_training(s_grid, action_starting_gridids)
# s1
s_grid_next = stateprocessor.to_grid_next_states(s_grid_train, next_state, action_idx_valid, env.city_time)
# Save transition to replay memory
if ii != 0:
# r1, c0
r_grid = stateprocessor.to_grid_rewards(action_idx_valid_prev, immediate_reward)
targets_batch = r_grid + gamma * target_estimator.predict(s_grid_next_prev)
# s0, a0, r1
replay.add(state_mat_prev, action_mat_prev, targets_batch, s_grid_next_prev)
state_mat_prev = s_grid_train
action_mat_prev = action_mat
context_prev = context
s_grid_next_prev = s_grid_next
action_idx_valid_prev = action_idx_valid
# c1
context = stateprocessor.compute_context(info[1])
# s1
curr_s = stateprocessor.utility_conver_states(next_state)
normalized_curr_s = stateprocessor.utility_normalize_states(curr_s)
s_grid = stateprocessor.to_grid_states(normalized_curr_s, env.city_time) # t0, s0
# Sample a minibatch from the replay memory and update q network training method1
if replay.curr_lens != 0:
for _ in np.arange(20):
fetched_batch = replay.sample()
mini_s, mini_a, mini_target, mini_next_s = fetched_batch
q_estimator.update(mini_s, mini_a, mini_target, learning_rate, global_step)
global_step += 1
# Perform gradient descent update
# book keeping
global_step += 1
all_rewards.append(r)
batch_reward_gmv.append(r)
order_response_rates.append(env.order_response_rate)
num_conflicts_drivers.append(collision_action(action_tuple))
curr_num_action = np.sum([aa[2] for aa in action_tuple]) if len(action_tuple) != 0 else 0
curr_num_actions.append(curr_num_action)
episode_reward = np.sum(batch_reward_gmv[1:])
episode_rewards.append(episode_reward)
n_iter_order_response_rate = np.mean(order_response_rates[1:])
order_response_rate_episode.append(n_iter_order_response_rate)
episode_conflicts_drivers.append(np.sum(num_conflicts_drivers[:-1]))
episode_dispatched_drivers.append(np.sum(curr_num_actions[:-1]))
print "iteration {} ********* reward {} order{} conflicts {} drivers {}".format(n_iter, episode_reward,
order_response_rate_episode[-1],
episode_conflicts_drivers[-1],
episode_dispatched_drivers[-1])
pickle.dump([episode_rewards, order_response_rate_episode, save_random_seed, episode_conflicts_drivers,
episode_dispatched_drivers], open(log_dir + "results.pkl", "w"))
if n_iter == 24:
break
# # training method 2.
# for _ in np.arange(4000):
# fetched_batch = replay.sample()
# mini_s, mini_a, mini_target, mini_next_s = fetched_batch
# q_estimator.update(mini_s, mini_a, mini_target, learning_rate, global_step)
# global_step += 1
# update target Q network
estimator_copy.make(sess)
saver.save(sess, log_dir+"model.ckpt")
================================================
FILE: run/run_baseline_nopolicy.py
================================================
import pickle, sys
sys.path.append("../")
from simulator.envs import *
################## Load data ###################################
dir_prefix = "/mnt/research/linkaixi/AllData/dispatch/"
current_time = time.strftime("%Y%m%d_%H-%M")
log_dir = dir_prefix + "dispatch_simulator/experiments/{}/".format(current_time)
mkdir_p(log_dir)
print "log dir is {}".format(log_dir)
data_dir = dir_prefix + "dispatch_realdata/data_for_simulator2017-07-24_2017-08-20/"
order_time_dist = []
order_price_dist = []
mapped_matrix_int = pickle.load(open(data_dir+"mapped_matrix_int.pkl", 'rb'))
order_num_dist = pickle.load(open(data_dir+"order_num_dist", 'rb'))
idle_driver_dist_time = pickle.load(open(data_dir+"idle_driver_dist_time", 'rb'))
idle_driver_location_mat = pickle.load(open(data_dir+"idle_driver_location_mat", 'rb'))
target_ids = pickle.load(open(data_dir+"target_grid_id.pkl", 'rb'))
onoff_driver_location_mat = pickle.load(open(data_dir + "onoff_driver_location_mat", 'rb'))
order_filename = dir_prefix + "dispatch_realdata/order_new_2017-07-24_2017-08-20/all_orders_target"
order_real = pickle.load(open(order_filename, 'rb'))
M, N = mapped_matrix_int.shape
print "finish load data"
################## Initialize env ###################################
n_side = 6
GAMMA = 0.9
l_max = 9
env = CityReal(mapped_matrix_int, order_num_dist,
idle_driver_dist_time, idle_driver_location_mat,
order_time_dist, order_price_dist,
l_max, M, N, n_side, 1/28.0, order_real, onoff_driver_location_mat)
log_dir = dir_prefix + "dispatch_simulator/experiments/{}/".format(current_time)
mkdir_p(log_dir)
temp = np.array(env.target_grids) + env.M * env.N
target_id_states = env.target_grids + temp.tolist()
print "******************* Finish generating one day order **********************"
print "******************* Starting runing no policy baseline **********************"
MAX_ITER = 50 # 10 iteration the Q-learning loss will converge.
is_plot_figure = False
city_time_start = 0
EP_LEN = 144
global_step = 0
city_time_end = city_time_start + EP_LEN
epsilon = 0.5
gamma = 0.9
learning_rate = 1e-3
prev_epsiode_reward = 0
curr_num_actions = []
all_rewards = []
order_response_rate_episode = []
value_table_sum = []
episode_rewards = []
num_conflicts_drivers = []
driver_numbers_episode = []
order_numbers_episode = []
T = 144
action_dim = 7
state_dim = env.n_valid_grids * 3 + T
record_all_order_response_rate = []
def compute_context(target_grids, info):
context = info.flatten()
context = [context[idx] for idx in target_grids]
return context
RATIO = 1
print "Start Running "
save_random_seed = []
episode_avaliables_vehicles = []
for n_iter in np.arange(10):
RANDOM_SEED = n_iter + MAX_ITER + 5
env.reset_randomseed(RANDOM_SEED)
save_random_seed.append(RANDOM_SEED)
batch_s, batch_a, batch_r = [], [], []
batch_reward_gmv = []
epsiode_reward = 0
num_dispatched_drivers = 0
driver_numbers = []
order_numbers = []
is_regenerate_order = 1
curr_state = env.reset_clean(generate_order=is_regenerate_order, ratio=RATIO, city_time=city_time_start)
driver_numbers.append(np.sum(curr_state[0]))
order_numbers.append(np.sum(curr_state[1]))
info = env.step_pre_order_assigin(curr_state)
context = compute_context(env.target_grids, np.array(info))
# record rewards to update the value table
episodes_immediate_rewards = []
order_response_rates = []
available_drivers = []
for ii in np.arange(EP_LEN + 1):
available_drivers.append(np.sum(context))
# ONE STEP: r0
next_state, r, info = env.step([], 2)
driver_numbers.append(np.sum(next_state[0]))
order_numbers.append(np.sum(next_state[1]))
context = compute_context(env.target_grids, np.array(info[1]))
# Perform gradient descent update
# book keeping
global_step += 1
all_rewards.append(r)
batch_reward_gmv.append(r)
order_response_rates.append(env.order_response_rate)
episode_reward = np.sum(batch_reward_gmv[1:])
episode_rewards.append(episode_reward)
driver_numbers_episode.append(np.sum(driver_numbers[:-1]))
order_numbers_episode.append(np.sum(order_numbers[:-1]))
episode_avaliables_vehicles.append(np.sum(available_drivers[:-1]))
n_iter_order_response_rate = np.mean(order_response_rates[1:])
order_response_rate_episode.append(n_iter_order_response_rate)
record_all_order_response_rate.append(order_response_rates)
print "******** iteration {} ********* reward {}, order response rate {} available vehicle {}".format(n_iter,
episode_reward,
n_iter_order_response_rate,
episode_avaliables_vehicles[-1])
pickle.dump([episode_rewards, order_response_rate_episode, save_random_seed,
driver_numbers_episode, order_numbers_episode, episode_avaliables_vehicles], open(log_dir + "results.pkl", "w"))
print "averaged available vehicles per time step: {}".format(np.mean(episode_avaliables_vehicles)/144.0)
================================================
FILE: run/run_cA2C.py
================================================
# -*- coding: utf-8 -*-
import pickle, sys
sys.path.append("../")
# from simulator.utilities import *
from algorithm.alg_utility import *
from simulator.envs import *
from shutil import copyfile
################## Load data ###################################
dir_prefix = "/mnt/research/linkaixi/AllData/dispatch/"
current_time = time.strftime("%Y%m%d_%H-%M")
log_dir = dir_prefix + "dispatch_simulator/experiments/{}/".format(current_time)
mkdir_p(log_dir)
print "log dir is {}".format(log_dir)
data_dir = dir_prefix + "dispatch_realdata/data_for_simulator/"
order_time_dist = []
order_price_dist = []
mapped_matrix_int = pickle.load(open(data_dir+"mapped_matrix_int.pkl", 'rb'))
order_num_dist = pickle.load(open(data_dir+"order_num_dist", 'rb'))
idle_driver_dist_time = pickle.load(open(data_dir+"idle_driver_dist_time", 'rb'))
idle_driver_location_mat = pickle.load(open(data_dir+"idle_driver_location_mat", 'rb'))
target_ids = pickle.load(open(data_dir+"target_grid_id.pkl", 'rb'))
onoff_driver_location_mat = pickle.load(open(data_dir + "onoff_driver_location_mat", 'rb'))
order_filename = dir_prefix + "dispatch_realdata/orders/all_orders_target"
order_real = pickle.load(open(order_filename, 'rb'))
M, N = mapped_matrix_int.shape
print "finish load data"
################## Initialize env ###################################
n_side = 6
GAMMA = 0.9
l_max = 9
env = CityReal(mapped_matrix_int, order_num_dist,
idle_driver_dist_time, idle_driver_location_mat,
order_time_dist, order_price_dist,
l_max, M, N, n_side, 1/28.0, order_real, onoff_driver_location_mat)
log_dir = dir_prefix + "dispatch_simulator/experiments/{}/".format(current_time)
temp = np.array(env.target_grids) + env.M * env.N
target_id_states = env.target_grids + temp.tolist()
curr_s = np.array(env.reset_clean()).flatten() # [0] driver dist; [1] order dist
curr_s = utility_conver_states(curr_s, target_id_states)
print "******************* Finish generating one day order **********************"
print "******************* Starting training Deep actor critic **********************"
from algorithm.cA2C import *
MAX_ITER = 50
is_plot_figure = False
city_time_start = 0
EP_LEN = 144
city_time_end = city_time_start + EP_LEN
epsilon = 0.5
gamma = 0.9
learning_rate = 1e-3
prev_epsiode_reward = 0
all_rewards = []
order_response_rate_episode = []
value_table_sum = []
episode_rewards = []
episode_conflicts_drivers = []
record_all_order_response_rate = []
T = 144
action_dim = 7
state_dim = env.n_valid_grids * 3 + T
# tf.reset_default_graph()
sess = tf.Session()
tf.set_random_seed(1)
q_estimator = Estimator(sess, action_dim,
state_dim,
env,
scope="q_estimator",
summaries_dir=log_dir)
sess.run(tf.global_variables_initializer())
replay = ReplayMemory(memory_size=1e+6, batch_size=int(3e+3))
policy_replay = policyReplayMemory(memory_size=1e+6, batch_size=int(3e+3))
stateprocessor = stateProcessor(target_id_states, env.target_grids, env.n_valid_grids)
restore = True
saver = tf.train.Saver()
# record_curr_state = []
# record_actions = []
save_random_seed = []
episode_dispatched_drivers = []
global_step1 = 0
global_step2 = 0
RATIO = 1
for n_iter in np.arange(25):
RANDOM_SEED = n_iter + MAX_ITER - 10
env.reset_randomseed(RANDOM_SEED)
save_random_seed.append(RANDOM_SEED)
batch_s, batch_a, batch_r = [], [], []
batch_reward_gmv = []
epsiode_reward = 0
num_dispatched_drivers = 0
# reset env
is_regenerate_order = 1
curr_state = env.reset_clean(generate_order=is_regenerate_order, ratio=RATIO, city_time=city_time_start)
info = env.step_pre_order_assigin(curr_state)
context = stateprocessor.compute_context(info)
curr_s = stateprocessor.utility_conver_states(curr_state)
normalized_curr_s = stateprocessor.utility_normalize_states(curr_s)
s_grid = stateprocessor.to_grid_states(normalized_curr_s, env.city_time) # t0, s0
# record rewards to update the value table
episodes_immediate_rewards = []
num_conflicts_drivers = []
curr_num_actions = []
order_response_rates = []
for ii in np.arange(EP_LEN + 1):
# record_curr_state.append(curr_state)
# INPUT: state, OUTPUT: action
action_tuple, valid_action_prob_mat, policy_state, action_choosen_mat, \
curr_state_value, curr_neighbor_mask, next_state_ids = q_estimator.action(s_grid, context, epsilon)
# a0
# ONE STEP: r0
next_state, r, info = env.step(action_tuple, 2)
# r0
immediate_reward = stateprocessor.reward_wrapper(info, curr_s)
# Save transition to replay memory
if ii != 0:
# r1, c0
r_grid = stateprocessor.to_grid_rewards(immediate_reward)
# s0, a0, r1 for value newtwork
targets_batch = q_estimator.compute_targets(action_mat_prev, s_grid, r_grid, gamma)
# advantage for policy network.
advantage = q_estimator.compute_advantage(curr_state_value_prev, next_state_ids_prev,
s_grid, r_grid, gamma)
replay.add(state_mat_prev, action_mat_prev, targets_batch, s_grid)
policy_replay.add(policy_state_prev, action_choosen_mat_prev, advantage, curr_neighbor_mask_prev)
# for updating value network
state_mat_prev = s_grid
action_mat_prev = valid_action_prob_mat
# for updating policy net
action_choosen_mat_prev = action_choosen_mat
curr_neighbor_mask_prev = curr_neighbor_mask
policy_state_prev = policy_state
# for computing advantage
curr_state_value_prev = curr_state_value
next_state_ids_prev = next_state_ids
# s1
curr_state = next_state
curr_s = stateprocessor.utility_conver_states(next_state)
normalized_curr_s = stateprocessor.utility_normalize_states(curr_s)
s_grid = stateprocessor.to_grid_states(normalized_curr_s, env.city_time) # t0, s0
# c1
context = stateprocessor.compute_context(info[1])
# training method 1.
# # # Sample a minibatch from the replay memory and update q network
# if replay.curr_lens != 0:
# # update policy network
# for _ in np.arange(30):
# batch_s, batch_a, batch_r, batch_mask = policy_replay.sample()
# q_estimator.update_policy(batch_s, batch_r.reshape([-1, 1]), batch_a, batch_mask, learning_rate,
# global_step2)
# global_step2 += 1
# Perform gradient descent update
# book keeping
global_step1 += 1
global_step2 += 1
all_rewards.append(r)
batch_reward_gmv.append(r)
order_response_rates.append(env.order_response_rate)
curr_num_action = np.sum([aa[2] for aa in action_tuple]) if len(action_tuple) != 0 else 0
curr_num_actions.append(curr_num_action)
num_conflicts_drivers.append(collision_action(action_tuple))
episode_reward = np.sum(batch_reward_gmv[1:])
episode_rewards.append(episode_reward)
n_iter_order_response_rate = np.mean(order_response_rates[1:])
order_response_rate_episode.append(n_iter_order_response_rate)
record_all_order_response_rate.append(order_response_rates)
episode_conflicts_drivers.append(np.sum(num_conflicts_drivers[:-1]))
episode_dispatched_drivers.append(np.sum(curr_num_actions[:-1]))
print "******** iteration {} ********* reward {}, order_response_rate {} number drivers {}, conflicts {}".format(n_iter, episode_reward,
n_iter_order_response_rate,
episode_dispatched_drivers[-1],
episode_conflicts_drivers[-1])
pickle.dump([episode_rewards, order_response_rate_episode, save_random_seed, episode_conflicts_drivers,
episode_dispatched_drivers], open(log_dir + "results.pkl", "w"))
if n_iter == 24:
break
# update value network
for _ in np.arange(4000):
batch_s, _, batch_r, _ = replay.sample()
iloss = q_estimator.update_value(batch_s, batch_r, 1e-3, global_step1)
global_step1 += 1
# training method 2
# update policy network
for _ in np.arange(4000):
batch_s, batch_a, batch_r, batch_mask = policy_replay.sample()
q_estimator.update_policy(batch_s, batch_r.reshape([-1, 1]), batch_a, batch_mask, learning_rate,
global_step2)
global_step2 += 1
saver.save(sess, log_dir+"model.ckpt")
if RANDOM_SEED == 54:
saver.save(sess, log_dir + "model_before_testing.ckpt")
================================================
FILE: run/run_cDQN.py
================================================
import pickle, sys
sys.path.append("../")
# from simulator.utilities import *
from algorithm.alg_utility import *
from simulator.envs import *
################## Load data ###################################
dir_prefix = "/mnt/research/linkaixi/AllData/dispatch/"
current_time = time.strftime("%Y%m%d_%H-%M")
log_dir = dir_prefix + "dispatch_simulator/experiments/{}/".format(current_time)
mkdir_p(log_dir)
print "log dir is {}".format(log_dir)
data_dir = dir_prefix + "dispatch_realdata/data_for_simulator/"
order_time_dist = []
order_price_dist = []
mapped_matrix_int = pickle.load(open(data_dir+"mapped_matrix_int.pkl", 'rb'))
order_num_dist = pickle.load(open(data_dir+"order_num_dist", 'rb'))
idle_driver_dist_time = pickle.load(open(data_dir+"idle_driver_dist_time", 'rb'))
idle_driver_location_mat = pickle.load(open(data_dir+"idle_driver_location_mat", 'rb'))
target_ids = pickle.load(open(data_dir+"target_grid_id.pkl", 'rb'))
onoff_driver_location_mat = pickle.load(open(data_dir + "onoff_driver_location_mat", 'rb'))
order_filename = dir_prefix + "dispatch_realdata/orders/all_orders_target"
order_real = pickle.load(open(order_filename, 'rb'))
M, N = mapped_matrix_int.shape
print "finish load data"
################## Initialize env ###################################
n_side = 6
GAMMA = 0.9
l_max = 9
env = CityReal(mapped_matrix_int, order_num_dist,
idle_driver_dist_time, idle_driver_location_mat,
order_time_dist, order_price_dist,
l_max, M, N, n_side, 1/28.0, order_real, onoff_driver_location_mat)
log_dir = dir_prefix + "dispatch_simulator/experiments/{}/".format(current_time)
temp = np.array(env.target_grids) + env.M * env.N
target_id_states = env.target_grids + temp.tolist()
curr_s = np.array(env.reset_clean()).flatten() # [0] driver dist; [1] order dist
curr_s = utility_conver_states(curr_s, target_id_states)
print "******************* Finish generating one day order **********************"
print "******************* Starting training Deep SARSA **********************"
from algorithm.cDQN import *
MAX_ITER = 50
is_plot_figure = False
city_time_start = 0
EP_LEN = 144
global_step = 0
city_time_end = city_time_start + EP_LEN
EPSILON = 0.8
gamma = 0.9
learning_rate = 1e-3
prev_epsiode_reward = 0
curr_num_actions = []
all_rewards = []
order_response_rate_episode = []
value_table_sum = []
episode_rewards = []
episode_conflicts_drivers = []
record_all_order_response_rate = []
T = 144
action_dim = 7
state_dim = env.n_valid_grids * 3 + T
# tf.reset_default_graph()
sess = tf.Session()
tf.set_random_seed(1)
q_estimator = Estimator(sess, action_dim,
state_dim,
env,
scope="q_estimator",
summaries_dir=log_dir)
target_estimator = Estimator(sess, action_dim, state_dim, env, scope="target_q")
sess.run(tf.global_variables_initializer())
estimator_copy = ModelParametersCopier(q_estimator, target_estimator)
replay = ReplayMemory(memory_size=1e+6, batch_size=int(3e+3))
stateprocessor = stateProcessor(target_id_states, env.target_grids, env.n_valid_grids)
RATIO = 1
saver = tf.train.Saver()
print "Start training contextual deep Q learning. "
save_random_seed = []
N_ITER_RUNS = 25
temp_value = 10
EPSILON_start = 0.5
EPSILON_end = 0.1
epsilon_decay_steps=15
epsilons = np.linspace(EPSILON_start, EPSILON_end, epsilon_decay_steps)
episode_dispatched_drivers = []
for n_iter in np.arange(N_ITER_RUNS):
RANDOM_SEED = n_iter + MAX_ITER - temp_value
env.reset_randomseed(RANDOM_SEED)
save_random_seed.append(RANDOM_SEED)
batch_s, batch_a, batch_r = [], [], []
batch_reward_gmv = []
epsiode_reward = 0
num_dispatched_drivers = 0
# reset env
# if n_iter % 1 == 0:
# is_regenerate_order = 1
# else:
# is_regenerate_order = 0
is_regenerate_order = 1
curr_state = env.reset_clean(generate_order=is_regenerate_order, ratio=RATIO, city_time=city_time_start)
info = env.step_pre_order_assigin(curr_state)
context = stateprocessor.compute_context(info)
curr_s = stateprocessor.utility_conver_states(curr_state)
normalized_curr_s = stateprocessor.utility_normalize_states(curr_s)
s_grid = stateprocessor.to_grid_states(normalized_curr_s, env.city_time) # t0, s0
# record rewards to update the value table
episodes_immediate_rewards = []
order_response_rates = []
# epsilon = EPSILON * (1 - np.max([n_iter, temp_value+5]) / N_ITER_RUNS) #testing staget 不改变epsilon
epsilon = epsilons[n_iter] if n_iter < 15 else EPSILON_end
num_conflicts_drivers = []
curr_num_actions = []
for ii in np.arange(EP_LEN + 1):
# INPUT: state, OUTPUT: action
action_tuple, valid_action_prob_mat = q_estimator.action(s_grid, context, epsilon)
# a0
# ONE STEP: r0
next_state, r, info = env.step(action_tuple, 2)
# r0
immediate_reward = stateprocessor.reward_wrapper(info, curr_s)
# Save transition to replay memory
if ii != 0:
# r1, c0
r_grid = stateprocessor.to_grid_rewards(immediate_reward)
# s0, a0, r1
targets_batch = target_estimator.compute_targets(action_mat_prev, s_grid, r_grid, gamma)
replay.add(state_mat_prev, action_mat_prev, targets_batch, s_grid)
state_mat_prev = s_grid
action_mat_prev = valid_action_prob_mat
# s1
curr_s = stateprocessor.utility_conver_states(next_state)
normalized_curr_s = stateprocessor.utility_normalize_states(curr_s)
s_grid = stateprocessor.to_grid_states(normalized_curr_s, env.city_time) # t0, s0
# c1
context = stateprocessor.compute_context(info[1])
# training method 2
# Sample a minibatch from the replay memory and update q network
# if replay.curr_lens != 0:
# for _ in np.arange(20):
# batch_s, _, batch_r, _ = replay.sample()
# iloss = q_estimator.update(batch_s, batch_r, 1e-3, global_step)
# global_step += 1
# print "******** city time {} *********".format(env.city_time)
# Perform gradient descent update
# book keeping
global_step += 1
all_rewards.append(r)
batch_reward_gmv.append(r)
order_response_rates.append(env.order_response_rate)
curr_num_action = np.sum([aa[2] for aa in action_tuple]) if len(action_tuple) != 0 else 0
curr_num_actions.append(curr_num_action)
num_conflicts_drivers.append(collision_action(action_tuple))
# training method 1
for _ in np.arange(4000):
batch_s, _, batch_r, _ = replay.sample()
iloss = q_estimator.update(batch_s, batch_r, 1e-3, global_step)
global_step += 1
# update target Q network
# if (global_step + 1) % 70 == 0:
estimator_copy.make(sess)
episode_reward = np.sum(batch_reward_gmv[1:])
episode_rewards.append(episode_reward)
n_iter_order_response_rate = np.mean(order_response_rates[1:])
order_response_rate_episode.append(n_iter_order_response_rate)
record_all_order_response_rate.append(order_response_rates)
episode_conflicts_drivers.append(np.sum(num_conflicts_drivers[:-1]))
episode_dispatched_drivers.append(np.sum(curr_num_actions[:-1]))
print "******** iteration {} ********* reward {}, order_response_rate {} number drivers {}, conflicts {}, epsilon {}".format(n_iter, episode_reward,
n_iter_order_response_rate,
episode_dispatched_drivers[-1],
episode_conflicts_drivers[-1],
epsilon)
pickle.dump([episode_rewards, order_response_rate_episode, save_random_seed, episode_conflicts_drivers, episode_dispatched_drivers], open(log_dir + "results.pkl", "w"))
saver.save(sess, log_dir+"model.ckpt")
================================================
FILE: simulator/__init__.py
================================================
================================================
FILE: simulator/envs.py
================================================
import os, sys, random, time
import logging
sys.path.append("../")
from objects import *
from utilities import *
# from algorithm import *
# current_time = time.strftime("%Y%m%d_%H-%M")
# log_dir = "/nfs/private/linkaixiang_i/data/dispatch_simulator/experiments/"+current_time + "/"
# mkdir_p(log_dir)
# logging.basicConfig(filename=log_dir +'logger_env.log', level=logging.INFO)
logger = logging.getLogger(__name__)
logger.setLevel(logging.DEBUG)
logger_ch = logging.StreamHandler()
logger_ch.setLevel(logging.DEBUG)
logger_ch.setFormatter(logging.Formatter(
'%(asctime)s[%(levelname)s][%(lineno)s:%(funcName)s]||%(message)s',
datefmt='%Y-%m-%d %H:%M:%S'))
logger.addHandler(logger_ch)
RANDOM_SEED = 0 # unit test use this random seed.
class CityReal:
'''A real city is consists of M*N grids '''
def __init__(self, mapped_matrix_int, order_num_dist, idle_driver_dist_time, idle_driver_location_mat, order_time_dist, order_price_dist,
l_max, M, N, n_side, probability=1.0/28, real_orders="", onoff_driver_location_mat="",
global_flag="global", time_interval=10):
"""
:param mapped_matrix_int: 2D matrix: each position is either -100 or grid id from order in real data.
:param order_num_dist: 144 [{node_id1: [mu, std]}, {node_id2: [mu, std]}, ..., {node_idn: [mu, std]}]
node_id1 is node the index in self.nodes
:param idle_driver_dist_time: [[mu1, std1], [mu2, std2], ..., [mu144, std144]] mean and variance of idle drivers in
the city at each time
:param idle_driver_location_mat: 144 x num_valid_grids matrix.
:param order_time_dist: [ 0.27380797,..., 0.00205766] The probs of order duration = 1 to 9
:param order_price_dist: [[10.17, 3.34], # mean and std of order's price, order durations = 10 minutes.
[15.02, 6.90], # mean and std of order's price, order durations = 20 minutes.
...,]
:param onoff_driver_location_mat: 144 x 504 x 2: 144 total time steps, num_valid_grids = 504.
mean and std of online driver number - offline driver number
onoff_driver_location_mat[t] = [[-0.625 2.92350389] <-- Corresponds to the grid in target_node_ids
[ 0.09090909 1.46398452]
[ 0.09090909 2.36596622]
[-1.2 2.05588586]...]
:param M:
:param N:
:param n_side:
:param time_interval:
:param l_max: The max-duration of an order
:return:
"""
# City.__init__(self, M, N, n_side, time_interval)
self.M = M # row numbers
self.N = N # column numbers
self.nodes = [Node(i) for i in xrange(M * N)] # a list of nodes: node id start from 0
self.drivers = {} # driver[driver_id] = driver_instance , driver_id start from 0
self.n_drivers = 0 # total idle number of drivers. online and not on service.
self.n_offline_drivers = 0 # total number of offline drivers.
self.construct_map_simulation(M, N, n_side)
self.city_time = 0
# self.idle_driver_distribution = np.zeros((M, N))
self.n_intervals = 1440 / time_interval
self.n_nodes = self.M * self.N
self.n_side = n_side
self.order_response_rate = 0
self.RANDOM_SEED = RANDOM_SEED
self.l_max = l_max # Start from 1. The max number of layers an order can across.
assert l_max <= M-1 and l_max <= N-1
assert 1 <= l_max <= 9 # Ignore orders less than 10 minutes and larger than 1.5 hours
self.target_grids = []
self.n_valid_grids = 0 # num of valid grid
self.nodes = [None for _ in np.arange(self.M * self.N)]
self.construct_node_real(mapped_matrix_int)
self.mapped_matrix_int = mapped_matrix_int
self.construct_map_real(n_side)
self.order_num_dist = order_num_dist
self.distribution_name = "Poisson"
self.idle_driver_dist_time = idle_driver_dist_time
self.idle_driver_location_mat = idle_driver_location_mat
self.order_time_dist = order_time_dist[:l_max]/np.sum(order_time_dist[:l_max])
self.order_price_dist = order_price_dist
target_node_ids = []
target_grids_sorted = np.sort(mapped_matrix_int[np.where(mapped_matrix_int > 0)])
for item in target_grids_sorted:
x, y = np.where(mapped_matrix_int == item)
target_node_ids.append(ids_2dto1d(x, y, M, N)[0])
self.target_node_ids = target_node_ids
# store valid note id. Sort by number of orders emerged. descending.
self.node_mapping = {}
self.construct_mapping()
self.real_orders = real_orders # 4 weeks' data
# [[92, 300, 143, 2, 13.2],...] origin grid, destination grid, start time, end time, price.
self.p = probability # sample probability
self.time_keys = [int(dt.strftime('%H%M')) for dt in
datetime_range(datetime(2017, 9, 1, 0), datetime(2017, 9, 2, 0),
timedelta(minutes=time_interval))]
self.day_orders = [] # one day's order.
self.onoff_driver_location_mat = onoff_driver_location_mat
# Stats
self.all_grids_on_number = 0 # current online # drivers.
self.all_grids_off_number = 0
self.out_grid_in_orders = np.zeros((self.n_intervals, len(self.target_grids)))
self.global_flag = global_flag
self.weights_layers_neighbors = [1.0, np.exp(-1), np.exp(-2)]
def construct_map_simulation(self, M, N, n):
"""Connect node to its neighbors based on a simulated M by N map
:param M: M row index matrix
:param N: N column index matrix
:param n: n - sided polygon
"""
for idx, current_node in enumerate(self.nodes):
if current_node is not None:
i, j = ids_1dto2d(idx, M, N)
current_node.set_neighbors(get_neighbor_list(i, j, M, N, n, self.nodes))
def construct_mapping(self):
"""
:return:
"""
target_grid_id = self.mapped_matrix_int[np.where(self.mapped_matrix_int>0)]
for g_id, n_id in zip(target_grid_id, self.target_grids):
self.node_mapping[g_id] = n_id
def construct_node_real(self, mapped_matrix_int):
""" Initialize node, only valid node in mapped_matrix_in will be initialized.
"""
row_inds, col_inds = np.where(mapped_matrix_int >= 0)
target_ids = [] # start from 0.
for x, y in zip(row_inds, col_inds):
node_id = ids_2dto1d(x, y, self.M, self.N)
self.nodes[node_id] = Node(node_id) # node id start from 0.
target_ids.append(node_id)
for x, y in zip(row_inds, col_inds):
node_id = ids_2dto1d(x, y, self.M, self.N)
self.nodes[node_id].get_layers_neighbors(self.l_max, self.M, self.N, self)
self.target_grids = target_ids
self.n_valid_grids = len(target_ids)
def construct_map_real(self, n_side):
"""Build node connection.
"""
for idx, current_node in enumerate(self.nodes):
i, j = ids_1dto2d(idx, self.M, self.N)
if current_node is not None:
current_node.set_neighbors(get_neighbor_list(i, j, self.M, self.N, n_side, self.nodes))
def initial_order_random(self, distribution_all, dis_paras_all):
""" Initialize order distribution
:param distribution: 'Poisson', 'Gaussian'
:param dis_paras: lambda, mu, sigma
"""
for idx, node in enumerate(self.nodes):
if node is not None:
node.order_distribution(distribution_all[idx], dis_paras_all[idx])
def get_observation(self):
next_state = np.zeros((2, self.M, self.N))
for _node in self.nodes:
if _node is not None:
row_id, column_id = ids_1dto2d(_node.get_node_index(), self.M, self.N)
next_state[0, row_id, column_id] = _node.idle_driver_num
next_state[1, row_id, column_id] = _node.order_num
return next_state
def get_num_idle_drivers(self):
""" Compute idle drivers
:return:
"""
temp_n_idle_drivers= 0
for _node in self.nodes:
if _node is not None:
temp_n_idle_drivers += _node.idle_driver_num
return temp_n_idle_drivers
def get_observation_driver_state(self):
""" Get idle driver distribution, computing #drivers from node.
:return:
"""
next_state = np.zeros((self.M, self.N))
for _node in self.nodes:
if _node is not None:
row_id, column_id = ids_1dto2d(_node.get_node_index(), self.M, self.N)
next_state[row_id, column_id] = _node.get_idle_driver_numbers_loop()
return next_state
def reset_randomseed(self, random_seed):
self.RANDOM_SEED = random_seed
def reset(self):
""" Return initial observation: get order distribution and idle driver distribution
"""
_M = self.M
_N = self.N
assert self.city_time == 0
# initialization drivers according to the distribution at time 0
num_idle_driver = self.utility_get_n_idle_drivers_real()
self.step_driver_online_offline_control(num_idle_driver)
# generate orders at first time step
distribution_name = [self.distribution_name]*(_M*_N)
distribution_param_dictionary = self.order_num_dist[self.city_time]
distribution_param = [0]*(_M*_N)
for key, value in distribution_param_dictionary.iteritems():
if self.distribution_name == 'Gaussian':
mu, sigma = value
distribution_param[key] = mu, sigma
elif self.distribution_name == 'Poisson':
mu = value[0]
distribution_param[key] = mu
else:
print "Wrong distribution"
self.initial_order_random(distribution_name, distribution_param)
self.step_generate_order_real()
return self.get_observation()
def reset_clean(self, generate_order=1, ratio=1, city_time=""):
""" 1. bootstrap oneday's order data.
2. clean current drivers and orders, regenerate new orders and drivers.
can reset anytime
:return:
"""
if city_time != "":
self.city_time = city_time
# clean orders and drivers
self.drivers = {} # driver[driver_id] = driver_instance , driver_id start from 0
self.n_drivers = 0 # total idle number of drivers. online and not on service.
self.n_offline_drivers = 0 # total number of offline drivers.
for node in self.nodes:
if node is not None:
node.clean_node()
# Generate one day's order.
if generate_order == 1:
self.utility_bootstrap_oneday_order()
# Init orders of current time step
moment = self.city_time % self.n_intervals
self.step_bootstrap_order_real(self.day_orders[moment])
# Init current driver distribution
if self.global_flag == "global":
num_idle_driver = self.utility_get_n_idle_drivers_real()
num_idle_driver = int(num_idle_driver * ratio)
else:
num_idle_driver = self.utility_get_n_idle_drivers_nodewise()
self.step_driver_online_offline_control_new(num_idle_driver)
return self.get_observation()
def utility_collect_offline_drivers_id(self):
"""count how many drivers are offline
:return: offline_drivers: a list of offline driver id
"""
count = 0 # offline driver num
offline_drivers = [] # record offline driver id
for key, _driver in self.drivers.iteritems():
if _driver.online is False:
count += 1
offline_drivers.append(_driver.get_driver_id())
return offline_drivers
def utility_get_n_idle_drivers_nodewise(self):
""" compute idle drivers.
:return:
"""
time = self.city_time % self.n_intervals
idle_driver_num = np.sum(self.idle_driver_location_mat[time])
return int(idle_driver_num)
def utility_add_driver_real_new(self, num_added_driver):
curr_idle_driver_distribution = self.get_observation()[0]
curr_idle_driver_distribution_resort = np.array(
[int(curr_idle_driver_distribution.flatten()[index]) for index in
self.target_node_ids])
idle_driver_distribution = self.idle_driver_location_mat[self.city_time % self.n_intervals, :]
idle_diff = idle_driver_distribution.astype(int) - curr_idle_driver_distribution_resort
idle_diff[np.where(idle_diff <= 0)] = 0
node_ids = np.random.choice(self.target_node_ids, size=[num_added_driver],
p=idle_diff/float(np.sum(idle_diff)))
n_total_drivers = len(self.drivers.keys())
for ii, node_id in enumerate(node_ids):
added_driver_id = n_total_drivers + ii
self.drivers[added_driver_id] = Driver(added_driver_id)
self.drivers[added_driver_id].set_position(self.nodes[node_id])
self.nodes[node_id].add_driver(added_driver_id, self.drivers[added_driver_id])
self.n_drivers += num_added_driver
def utility_add_driver_real_new_offlinefirst(self, num_added_driver):
# curr_idle_driver_distribution = self.get_observation()[0][np.where(self.mapped_matrix_int > 0)]
curr_idle_driver_distribution = self.get_observation()[0]
curr_idle_driver_distribution_resort = np.array([int(curr_idle_driver_distribution.flatten()[index]) for index in
self.target_node_ids])
idle_driver_distribution = self.idle_driver_location_mat[self.city_time % self.n_intervals, :]
idle_diff = idle_driver_distribution.astype(int) - curr_idle_driver_distribution_resort
idle_diff[np.where(idle_diff <= 0)] = 0
if float(np.sum(idle_diff)) == 0:
return
np.random.seed(self.RANDOM_SEED)
node_ids = np.random.choice(self.target_node_ids, size=[num_added_driver],
p=idle_diff/float(np.sum(idle_diff)))
for ii, node_id in enumerate(node_ids):
if self.nodes[node_id].offline_driver_num > 0:
self.nodes[node_id].set_offline_driver_online()
self.n_drivers += 1
self.n_offline_drivers -= 1
else:
n_total_drivers = len(self.drivers.keys())
added_driver_id = n_total_drivers
self.drivers[added_driver_id] = Driver(added_driver_id)
self.drivers[added_driver_id].set_position(self.nodes[node_id])
self.nodes[node_id].add_driver(added_driver_id, self.drivers[added_driver_id])
self.n_drivers += 1
def utility_add_driver_real_nodewise(self, node_id, num_added_driver):
while num_added_driver > 0:
if self.nodes[node_id].offline_driver_num > 0:
self.nodes[node_id].set_offline_driver_online()
self.n_drivers += 1
self.n_offline_drivers -= 1
else:
n_total_drivers = len(self.drivers.keys())
added_driver_id = n_total_drivers
self.drivers[added_driver_id] = Driver(added_driver_id)
self.drivers[added_driver_id].set_position(self.nodes[node_id])
self.nodes[node_id].add_driver(added_driver_id, self.drivers[added_driver_id])
self.n_drivers += 1
num_added_driver -= 1
def utility_set_drivers_offline_real_nodewise(self, node_id, n_drivers_to_off):
while n_drivers_to_off > 0:
if self.nodes[node_id].idle_driver_num > 0:
self.nodes[node_id].set_idle_driver_offline_random()
self.n_drivers -= 1
self.n_offline_drivers += 1
n_drivers_to_off -= 1
self.all_grids_off_number += 1
else:
break
def utility_set_drivers_offline_real_new(self, n_drivers_to_off):
curr_idle_driver_distribution = self.get_observation()[0]
curr_idle_driver_distribution_resort = np.array([int(curr_idle_driver_distribution.flatten()[index])
for index in self.target_node_ids])
# historical idle driver distribution
idle_driver_distribution = self.idle_driver_location_mat[self.city_time % self.n_intervals, :]
# diff of curr idle driver distribution and history
idle_diff = curr_idle_driver_distribution_resort - idle_driver_distribution.astype(int)
idle_diff[np.where(idle_diff <= 0)] = 0
n_drivers_can_be_off = int(np.sum(curr_idle_driver_distribution_resort[np.where(idle_diff >= 0)]))
if n_drivers_to_off > n_drivers_can_be_off:
n_drivers_to_off = n_drivers_can_be_off
sum_idle_diff = np.sum(idle_diff)
if sum_idle_diff == 0:
return
np.random.seed(self.RANDOM_SEED)
node_ids = np.random.choice(self.target_node_ids, size=[n_drivers_to_off],
p=idle_diff / float(sum_idle_diff))
for ii, node_id in enumerate(node_ids):
if self.nodes[node_id].idle_driver_num > 0:
self.nodes[node_id].set_idle_driver_offline_random()
self.n_drivers -= 1
self.n_offline_drivers += 1
n_drivers_to_off -= 1
def utility_bootstrap_oneday_order(self):
num_all_orders = len(self.real_orders)
index_sampled_orders = np.where(np.random.binomial(1, self.p, num_all_orders) == 1)
one_day_orders = self.real_orders[index_sampled_orders]
self.out_grid_in_orders = np.zeros((self.n_intervals, len(self.target_grids)))
day_orders = [[] for _ in np.arange(self.n_intervals)]
for iorder in one_day_orders:
# iorder: [92, 300, 143, 2, 13.2]
start_time = int(iorder[2])
if iorder[0] not in self.node_mapping.keys() and iorder[1] not in self.node_mapping.keys():
continue
start_node = self.node_mapping.get(iorder[0], -100)
end_node = self.node_mapping.get(iorder[1], -100)
duration = int(iorder[3])
price = iorder[4]
if start_node == -100:
column_index = self.target_grids.index(end_node)
self.out_grid_in_orders[(start_time + duration) % self.n_intervals, column_index] += 1
continue
day_orders[start_time].append([start_node, end_node, start_time, duration, price])
self.day_orders = day_orders
def step_driver_status_control(self):
# Deal with orders finished at time T=1, check driver status. finish order, set back to off service
for key, _driver in self.drivers.iteritems():
_driver.status_control_eachtime(self)
moment = self.city_time % self.n_intervals
orders_to_on_drivers = self.out_grid_in_orders[moment, :]
for idx, item in enumerate(orders_to_on_drivers):
if item != 0:
node_id = self.target_grids[idx]
self.utility_add_driver_real_nodewise(node_id, int(item))
def step_driver_online_offline_nodewise(self):
""" node wise control driver online offline
:return:
"""
moment = self.city_time % self.n_intervals
curr_onoff_distribution = self.onoff_driver_location_mat[moment]
self.all_grids_on_number = 0
self.all_grids_off_number = 0
for idx, target_node_id in enumerate(self.target_node_ids):
curr_mu = curr_onoff_distribution[idx, 0]
curr_sigma = curr_onoff_distribution[idx, 1]
on_off_number = np.round(np.random.normal(curr_mu, curr_sigma, 1)[0]).astype(int)
if on_off_number > 0:
self.utility_add_driver_real_nodewise(target_node_id, on_off_number)
self.all_grids_on_number += on_off_number
elif on_off_number < 0:
self.utility_set_drivers_offline_real_nodewise(target_node_id, abs(on_off_number))
else:
pass
def step_driver_online_offline_control_new(self, n_idle_drivers):
""" control the online offline status of drivers
:param n_idle_drivers: the number of idle drivers expected at current moment
:return:
"""
offline_drivers = self.utility_collect_offline_drivers_id()
self.n_offline_drivers = len(offline_drivers)
if n_idle_drivers > self.n_drivers:
self.utility_add_driver_real_new_offlinefirst(n_idle_drivers - self.n_drivers)
elif n_idle_drivers < self.n_drivers:
self.utility_set_drivers_offline_real_new(self.n_drivers - n_idle_drivers)
else:
pass
def step_driver_online_offline_control(self, n_idle_drivers):
""" control the online offline status of drivers
:param n_idle_drivers: the number of idle drivers expected at current moment
:return:
"""
offline_drivers = self.utility_collect_offline_drivers_id()
self.n_offline_drivers = len(offline_drivers)
if n_idle_drivers > self.n_drivers:
# bring drivers online.
while self.n_drivers < n_idle_drivers:
if self.n_offline_drivers > 0:
for ii in np.arange(self.n_offline_drivers):
self.drivers[offline_drivers[ii]].set_online()
self.n_drivers += 1
self.n_offline_drivers -= 1
if self.n_drivers == n_idle_drivers:
break
self.utility_add_driver_real_new(n_idle_drivers - self.n_drivers)
elif n_idle_drivers < self.n_drivers:
self.utility_set_drivers_offline_real_new(self.n_drivers - n_idle_drivers)
else:
pass
def utility_get_n_idle_drivers_real(self):
""" control the number of idle drivers in simulator;
:return:
"""
time = self.city_time % self.n_intervals
mean, std = self.idle_driver_dist_time[time]
np.random.seed(self.city_time)
return np.round(np.random.normal(mean, std, 1)[0]).astype(int)
def utility_set_neighbor_weight(self, weights):
self.weights_layers_neighbors = weights
def step_generate_order_real(self):
# generate order at t + 1
for node in self.nodes:
if node is not None:
node_id = node.get_node_index()
# generate orders start from each node
random_seed = node.get_node_index() + self.city_time
node.generate_order_real(self.l_max, self.order_time_dist, self.order_price_dist,
self.city_time, self.nodes, random_seed)
def step_bootstrap_order_real(self, day_orders_t):
for iorder in day_orders_t:
start_node_id = iorder[0]
end_node_id = iorder[1]
start_node = self.nodes[start_node_id]
if end_node_id in self.target_grids:
end_node = self.nodes[end_node_id]
else:
end_node = None
start_node.add_order_real(self.city_time, end_node, iorder[3], iorder[4])
def step_assign_order(self):
reward = 0 # R_{t+1}
all_order_num = 0
finished_order_num = 0
for node in self.nodes:
if node is not None:
node.remove_unfinished_order(self.city_time)
reward_node, all_order_num_node, finished_order_num_node = node.simple_order_assign_real(self.city_time, self)
reward += reward_node
all_order_num += all_order_num_node
finished_order_num += finished_order_num_node
if all_order_num != 0:
self.order_response_rate = finished_order_num/float(all_order_num)
else:
self.order_response_rate = -1
return reward
def step_assign_order_broadcast_neighbor_reward_update(self):
""" Consider the orders whose destination or origin is not in the target region
:param num_layers:
:param weights_layers_neighbors: [1, 0.5, 0.25, 0.125]
:return:
"""
node_reward = np.zeros((len(self.nodes)))
neighbor_reward = np.zeros((len(self.nodes)))
# First round broadcast
reward = 0 # R_{t+1}
all_order_num = 0
finished_order_num = 0
for node in self.nodes:
if node is not None:
reward_node, all_order_num_node, finished_order_num_node = node.simple_order_assign_real(self.city_time, self)
reward += reward_node
all_order_num += all_order_num_node
finished_order_num += finished_order_num_node
node_reward[node.get_node_index()] += reward_node
# Second round broadcast
for node in self.nodes:
if node is not None:
if node.order_num != 0:
reward_node_broadcast, finished_order_num_node_broadcast \
= node.simple_order_assign_broadcast_update(self, neighbor_reward)
reward += reward_node_broadcast
finished_order_num += finished_order_num_node_broadcast
node_reward = node_reward + neighbor_reward
if all_order_num != 0:
self.order_response_rate = finished_order_num/float(all_order_num)
else:
self.order_response_rate = -1
return reward, [node_reward, neighbor_reward]
def step_remove_unfinished_orders(self):
for node in self.nodes:
if node is not None:
node.remove_unfinished_order(self.city_time)
def step_pre_order_assigin(self, next_state):
remain_drivers = next_state[0] - next_state[1]
remain_drivers[remain_drivers < 0] = 0
remain_orders = next_state[1] - next_state[0]
remain_orders[remain_orders < 0] = 0
if np.sum(remain_orders) == 0 or np.sum(remain_drivers) == 0:
context = np.array([remain_drivers, remain_orders])
return context
remain_orders_1d = remain_orders.flatten()
remain_drivers_1d = remain_drivers.flatten()
for node in self.nodes:
if node is not None:
curr_node_id = node.get_node_index()
if remain_orders_1d[curr_node_id] != 0:
for neighbor_node in node.neighbors:
if neighbor_node is not None:
neighbor_id = neighbor_node.get_node_index()
a = remain_orders_1d[curr_node_id]
b = remain_drivers_1d[neighbor_id]
remain_orders_1d[curr_node_id] = max(a-b, 0)
remain_drivers_1d[neighbor_id] = max(b-a, 0)
if remain_orders_1d[curr_node_id] == 0:
break
context = np.array([remain_drivers_1d.reshape(self.M, self.N),
remain_orders_1d.reshape(self.M, self.N)])
return context
def step_dispatch_invalid(self, dispatch_actions):
""" If a
:param dispatch_actions:
:return:
"""
save_remove_id = []
for action in dispatch_actions:
start_node_id, end_node_id, num_of_drivers = action
if self.nodes[start_node_id] is None or num_of_drivers == 0:
continue # not a feasible action
if self.nodes[start_node_id].get_driver_numbers() < num_of_drivers:
num_of_drivers = self.nodes[start_node_id].get_driver_numbers()
if end_node_id < 0:
for _ in np.arange(num_of_drivers):
self.nodes[start_node_id].set_idle_driver_offline_random()
self.n_drivers -= 1
self.n_offline_drivers += 1
self.all_grids_off_number += 1
continue
if self.nodes[end_node_id] is None:
for _ in np.arange(num_of_drivers):
self.nodes[start_node_id].set_idle_driver_offline_random()
self.n_drivers -= 1
self.n_offline_drivers += 1
self.all_grids_off_number += 1
continue
if self.nodes[end_node_id] not in self.nodes[start_node_id].neighbors:
raise ValueError('City:step(): not a feasible dispatch')
for _ in np.arange(num_of_drivers):
# t = 1 dispatch start, idle driver decrease
remove_driver_id = self.nodes[start_node_id].remove_idle_driver_random()
save_remove_id.append((end_node_id, remove_driver_id))
self.drivers[remove_driver_id].set_position(None)
self.drivers[remove_driver_id].set_offline_for_start_dispatch()
self.n_drivers -= 1
return save_remove_id
def step_add_dispatched_drivers(self, save_remove_id):
# drivers dispatched at t, arrived at t + 1
for destination_node_id, arrive_driver_id in save_remove_id:
self.drivers[arrive_driver_id].set_position(self.nodes[destination_node_id])
self.drivers[arrive_driver_id].set_online_for_finish_dispatch()
self.nodes[destination_node_id].add_driver(arrive_driver_id, self.drivers[arrive_driver_id])
self.n_drivers += 1
def step_increase_city_time(self):
self.city_time += 1
# set city time of drivers
for driver_id, driver in self.drivers.iteritems():
driver.set_city_time(self.city_time)
def step(self, dispatch_actions, generate_order=1):
info = []
'''**************************** T = 1 ****************************'''
# Loop over all dispatch action, change the driver distribution
save_remove_id = self.step_dispatch_invalid(dispatch_actions)
# When the drivers go to invalid grid, set them offline.
reward, reward_node = self.step_assign_order_broadcast_neighbor_reward_update()
'''**************************** T = 2 ****************************'''
# increase city time t + 1
self.step_increase_city_time()
self.step_driver_status_control() # drivers finish order become available again.
# drivers dispatched at t, arrived at t + 1, become available at t+1
self.step_add_dispatched_drivers(save_remove_id)
# generate order at t + 1
if generate_order == 1:
self.step_generate_order_real()
else:
moment = self.city_time % self.n_intervals
self.step_bootstrap_order_real(self.day_orders[moment])
# offline online control;
self.step_driver_online_offline_nodewise()
self.step_remove_unfinished_orders()
# get states S_{t+1} [driver_dist, order_dist]
next_state = self.get_observation()
context = self.step_pre_order_assigin(next_state)
info = [reward_node, context]
return next_state, reward, info
================================================
FILE: simulator/objects.py
================================================
import numpy as np
from abc import ABCMeta, abstractmethod
from utilities import *
class Distribution():
''' Define the distribution from which sample the orders'''
__metaclass__ = ABCMeta # python 2.7
@abstractmethod
def sample(self):
pass
class PoissonDistribution(Distribution):
def __init__(self, lam):
self._lambda = lam
def sample(self, seed=0):
np.random.seed(seed)
return np.random.poisson(self._lambda, 1)[0]
class GaussianDistribution(Distribution):
def __init__(self, args):
mu, sigma = args
self.mu = mu # mean
self.sigma = sigma # standard deviation
def sample(self, seed=0):
np.random.seed(seed)
return np.random.normal(self.mu, self.sigma, 1)[0]
class Node(object):
__slots__ = ('neighbors', '_index', 'orders', 'drivers',
'order_num', 'idle_driver_num', 'offline_driver_num'
'order_generator', 'offline_driver_num', 'order_generator',
'n_side', 'layers_neighbors', 'layers_neighbors_id')
def __init__(self, index):
# private
self._index = index # unique node index.
# public
self.neighbors = [] # a list of nodes that neighboring the Nodes
self.orders = [] # a list of orders
self.drivers = {} # a dictionary of driver objects contained in this node
self.order_num = 0
self.idle_driver_num = 0 # number of idle drivers in this node
self.offline_driver_num = 0
self.order_generator = None
self.n_side = 0 # the topology is a n-sided map
self.layers_neighbors = [] # layer 1 indices: layers_neighbors[0] = [[1,1], [0, 1], ...],
# layer 2 indices layers_neighbors[1]
self.layers_neighbors_id = [] # layer 1: layers_neighbors_id[0] = [2, 1,.]
def clean_node(self):
self.orders = []
self.order_num = 0
self.drivers = {}
self.idle_driver_num = 0
self.offline_driver_num = 0
def get_layers_neighbors(self, l_max, M, N, env):
x, y = ids_1dto2d(self.get_node_index(), M, N)
self.layers_neighbors = get_layers_neighbors(x, y, l_max, M, N)
for layer_neighbors in self.layers_neighbors:
temp = []
for item in layer_neighbors:
x, y = item
node_id = ids_2dto1d(x, y, M, N)
if env.nodes[node_id] is not None:
temp.append(node_id)
self.layers_neighbors_id.append(temp)
def get_node_index(self):
return self._index
def get_driver_numbers(self):
return self.idle_driver_num
def get_idle_driver_numbers_loop(self):
temp_idle_driver = 0
for key, driver in self.drivers.iteritems():
if driver.onservice is False and driver.online is True:
temp_idle_driver += 1
return temp_idle_driver
def get_off_driver_numbers_loop(self):
temp_idle_driver = 0
for key, driver in self.drivers.iteritems():
if driver.onservice is False and driver.online is False:
temp_idle_driver += 1
return temp_idle_driver
def order_distribution(self, distribution, dis_paras):
if distribution == 'Poisson':
self.order_generator = PoissonDistribution(dis_paras)
elif distribution == 'Gaussian':
self.order_generator = GaussianDistribution(dis_paras)
else:
pass
def generate_order_random(self, city_time, nodes, seed):
"""Generate new orders at each time step
"""
num_order_t = self.order_generator.sample(seed)
self.order_num += num_order_t
for ii in np.arange(num_order_t):
price = np.random.normal(50, 5, 1)[0]
price = 10 if price < 0 else price
current_node_id = self.get_node_index()
destination_node = [kk for kk in np.arange(len(nodes)) if kk != current_node_id]
self.orders.append(Order(nodes[current_node_id],
nodes[np.random.choice(destination_node, 1)[0]],
city_time,
# city_time + np.random.choice(5, 1)[0]+1,
np.random.choice(2, 1)[0]+1, # duration
price, 1))
return
def generate_order_real(self, l_max, order_time_dist, order_price_dist, city_time, nodes, seed):
"""Generate new orders at each time step
"""
num_order_t = self.order_generator.sample(seed)
self.order_num += num_order_t
for ii in np.arange(num_order_t):
if l_max == 1:
duration = 1
else:
duration = np.random.choice(np.arange(1, l_max+1), p=order_time_dist)
price_mean, price_std = order_price_dist[duration-1]
price = np.random.normal(price_mean, price_std, 1)[0]
price = price if price > 0 else price_mean
current_node_id = self.get_node_index()
destination_node = []
for jj in np.arange(duration):
for kk in self.layers_neighbors_id[jj]:
if nodes[kk] is not None:
destination_node.append(kk)
self.orders.append(Order(nodes[current_node_id],
nodes[np.random.choice(destination_node, 1)[0]],
city_time,
duration,
price, 1))
return
def add_order_real(self, city_time, destination_node, duration, price):
current_node_id = self.get_node_index()
self.orders.append(Order(self,
destination_node,
city_time,
duration,
price, 0))
self.order_num += 1
def set_neighbors(self, nodes_list):
self.neighbors = nodes_list
self.n_side = len(nodes_list)
def remove_idle_driver_random(self):
"""Randomly remove one idle driver from current grid"""
removed_driver_id = "NA"
for key, item in self.drivers.iteritems():
if item.onservice is False and item.online is True:
self.remove_driver(key)
removed_driver_id = key
if removed_driver_id != "NA":
break
assert removed_driver_id != "NA"
return removed_driver_id
def set_idle_driver_offline_random(self):
"""Randomly set one idle driver offline"""
removed_driver_id = "NA"
for key, item in self.drivers.iteritems():
if item.onservice is False and item.online is True:
item.set_offline()
removed_driver_id = key
if removed_driver_id != "NA":
break
assert removed_driver_id != "NA"
return removed_driver_id
def set_offline_driver_online(self):
online_driver_id = "NA"
for key, item in self.drivers.iteritems():
if item.onservice is False and item.online is False:
item.set_online()
online_driver_id = key
if online_driver_id != "NA":
break
assert online_driver_id != "NA"
return online_driver_id
def get_driver_random(self):
"""Randomly get one driver"""
assert self.idle_driver_num > 0
get_driver_id = 0
for key in self.drivers.iterkeys():
get_driver_id = key
break
return self.drivers[get_driver_id]
def remove_driver(self, driver_id):
removed_driver = self.drivers.pop(driver_id, None)
self.idle_driver_num -= 1
if removed_driver is None:
raise ValueError('Nodes.remove_driver: Remove a driver that is not in this node')
return removed_driver
def add_driver(self, driver_id, driver):
self.drivers[driver_id] = driver
self.idle_driver_num += 1
def remove_unfinished_order(self, city_time):
un_finished_order_index = []
for idx, o in enumerate(self.orders):
# order un served
if o.get_wait_time()+o.get_begin_time() < city_time:
un_finished_order_index.append(idx)
# order completed
if o.get_assigned_time() + o.get_duration() == city_time and o.get_assigned_time() != -1:
un_finished_order_index.append(idx)
if len(un_finished_order_index) != 0:
# remove unfinished orders
self.orders = [i for j, i in enumerate(self.orders) if j not in un_finished_order_index]
self.order_num = len(self.orders)
def simple_order_assign(self, city_time, city):
reward = 0
num_assigned_order = min(self.order_num, self.idle_driver_num)
served_order_index = []
for idx in np.arange(num_assigned_order):
order_to_serve = self.orders[idx]
order_to_serve.set_assigned_time(city_time)
self.order_num -= 1
reward += order_to_serve.get_price()
served_order_index.append(idx)
for key, assigned_driver in self.drivers.iteritems():
if assigned_driver.onservice is False and assigned_driver.online is True:
assigned_driver.take_order(order_to_serve)
removed_driver = self.drivers.pop(assigned_driver.get_driver_id(), None)
assert removed_driver is not None
city.n_drivers -= 1
break
all_order_num = len(self.orders)
finished_order_num = len(served_order_index)
# remove served orders
self.orders = [i for j, i in enumerate(self.orders) if j not in served_order_index]
assert self.order_num == len(self.orders)
return reward, all_order_num, finished_order_num
def simple_order_assign_real(self, city_time, city):
reward = 0
num_assigned_order = min(self.order_num, self.idle_driver_num)
served_order_index = []
for idx in np.arange(num_assigned_order):
order_to_serve = self.orders[idx]
order_to_serve.set_assigned_time(city_time)
self.order_num -= 1
reward += order_to_serve.get_price()
served_order_index.append(idx)
for key, assigned_driver in self.drivers.iteritems():
if assigned_driver.onservice is False and assigned_driver.online is True:
if order_to_serve.get_end_position() is not None:
assigned_driver.take_order(order_to_serve)
removed_driver = self.drivers.pop(assigned_driver.get_driver_id(), None)
assert removed_driver is not None
else:
assigned_driver.set_offline() # order destination is not in target region
city.n_drivers -= 1
break
all_order_num = len(self.orders)
finished_order_num = len(served_order_index)
# remove served orders
self.orders = [i for j, i in enumerate(self.orders) if j not in served_order_index]
assert self.order_num == len(self.orders)
return reward, all_order_num, finished_order_num
def simple_order_assign_broadcast_update(self, city, neighbor_node_reward):
assert self.idle_driver_num == 0
reward = 0
num_finished_orders = 0
for neighbor_node in self.neighbors:
if neighbor_node is not None and neighbor_node.idle_driver_num > 0:
num_assigned_order = min(self.order_num, neighbor_node.idle_driver_num)
rr = self.utility_assign_orders_neighbor(city, neighbor_node, num_assigned_order)
reward += rr
neighbor_node_reward[neighbor_node.get_node_index()] += rr
num_finished_orders += num_assigned_order
if self.order_num == 0:
break
assert self.order_num == len(self.orders)
return reward, num_finished_orders
def utility_assign_orders_neighbor(self, city, neighbor_node, num_assigned_order):
served_order_index = []
reward = 0
curr_city_time = city.city_time
for idx in np.arange(num_assigned_order):
order_to_serve = self.orders[idx]
order_to_serve.set_assigned_time(curr_city_time)
self.order_num -= 1
reward += order_to_serve.get_price()
served_order_index.append(idx)
for key, assigned_driver in neighbor_node.drivers.iteritems():
if assigned_driver.onservice is False and assigned_driver.online is True:
if order_to_serve.get_end_position() is not None:
assigned_driver.take_order(order_to_serve)
removed_driver = neighbor_node.drivers.pop(assigned_driver.get_driver_id(), None)
assert removed_driver is not None
else:
assigned_driver.set_offline()
city.n_drivers -= 1
break
# remove served orders
self.orders = [i for j, i in enumerate(self.orders) if j not in served_order_index]
assert self.order_num == len(self.orders)
return reward
class Driver(object):
__slots__ = ("online", "onservice", 'order', 'node', 'city_time', '_driver_id')
def __init__(self, driver_id):
self.online = True
self.onservice = False
self.order = None # the order this driver is serving
self.node = None # the node that contain this driver.
self.city_time = 0 # track the current system time
# private
self._driver_id = driver_id # unique driver id.
def set_position(self, node):
self.node = node
def set_order_start(self, order):
self.order = order
def set_order_finish(self):
self.order = None
self.onservice = False
def get_driver_id(self):
return self._driver_id
def update_city_time(self):
self.city_time += 1
def set_city_time(self, city_time):
self.city_time = city_time
def set_offline(self):
assert self.onservice is False and self.online is True
self.online = False
self.node.idle_driver_num -= 1
self.node.offline_driver_num += 1
def set_offline_for_start_dispatch(self):
assert self.onservice is False
self.online = False
def set_online(self):
assert self.onservice is False
self.online = True
self.node.idle_driver_num += 1
self.node.offline_driver_num -= 1
def set_online_for_finish_dispatch(self):
self.online = True
assert self.onservice is False
def take_order(self, order):
""" take order, driver show up at destination when order is finished
"""
assert self.online == True
self.set_order_start(order)
self.onservice = True
self.node.idle_driver_num -= 1
def status_control_eachtime(self, city):
assert self.city_time == city.city_time
if self.onservice is True:
assert self.online is True
order_end_time = self.order.get_assigned_time() + self.order.get_duration()
if self.city_time == order_end_time:
self.set_position(self.order.get_end_position())
self.set_order_finish()
self.node.add_driver(self._driver_id, self)
city.n_drivers += 1
elif self.city_time < order_end_time:
pass
else:
raise ValueError('Driver: status_control_eachtime(): order end time less than city time')
class Order(object):
__slots__ = ('_begin_p', '_end_p', '_begin_t',
'_t', '_p', '_waiting_time', '_assigned_time')
def __init__(self, begin_position, end_position, begin_time, duration, price, wait_time):
self._begin_p = begin_position # node
self._end_p = end_position # node
self._begin_t = begin_time
# self._end_t = end_time
self._t = duration # the duration of order.
self._p = price
self._waiting_time = wait_time # a order can last for "wait_time" to be taken
self._assigned_time = -1
def get_begin_position(self):
return self._begin_p
def get_begin_position_id(self):
return self._begin_p.get_node_index()
def get_end_position(self):
return self._end_p
def get_begin_time(self):
return self._begin_t
def set_assigned_time(self, city_time):
self._assigned_time = city_time
def get_assigned_time(self):
return self._assigned_time
# def get_end_time(self):
# return self._end_t
def get_duration(self):
return self._t
def get_price(self):
return self._p
def get_wait_time(self):
return self._waiting_time
================================================
FILE: simulator/utilities.py
================================================
import numpy as np
import os
import errno
from datetime import datetime, timedelta
def datetime_range(start, end, delta):
current = start
while current < end:
yield current
current += delta
def mkdir_p(path):
try:
os.makedirs(path)
except OSError as exc: # Python >2.5
if exc.errno == errno.EEXIST and os.path.isdir(path):
pass
else:
raise
def ids_2dto1d(i, j, M, N):
'''
convert (i,j) in a M by N matrix to index in M*N list. (row wise)
matrix: [[1,2,3], [4, 5, 6]]
list: [0, 1, 2, 3, 4, 5, 6]
index start from 0
'''
assert 0 <= i < M and 0 <= j < N
index = i * N + j
return index
def ids_1dto2d(ids, M, N):
''' inverse of ids_2dto1d(i, j, M, N)
index start from 0
'''
i = ids / N
j = ids - N * i
return (i, j)
def get_neighbor_list(i, j, M, N, n, nodes):
''' n: n-sided polygon, construct for a 2d map
1
6 2
center
5 3
4
return index of neighbor 1, 2, 3, 4, 5,6 in the matrix
'''
neighbor_list = [None] * n
if n == 6:
# hexagonal
if j % 2 == 0:
if i - 1 >= 0:
neighbor_list[0] = nodes[ids_2dto1d(i-1, j, M, N)]
if j + 1 < N:
neighbor_list[1] = nodes[ids_2dto1d(i, j+1, M, N)]
if i + 1 < M and j + 1 < N:
neighbor_list[2] = nodes[ids_2dto1d(i+1, j+1, M, N)]
if i + 1 < M:
neighbor_list[3] = nodes[ids_2dto1d(i+1, j, M, N)]
if i + 1 < M and j - 1 >= 0:
neighbor_list[4] = nodes[ids_2dto1d(i+1, j-1, M, N)]
if j - 1 >= 0:
neighbor_list[5] = nodes[ids_2dto1d(i, j-1, M, N)]
elif j % 2 == 1:
if i - 1 >= 0:
neighbor_list[0] = nodes[ids_2dto1d(i-1, j, M, N)]
if i - 1 >= 0 and j + 1 < N:
neighbor_list[1] = nodes[ids_2dto1d(i-1, j+1, M, N)]
if j + 1 < N:
neighbor_list[2] = nodes[ids_2dto1d(i, j+1, M, N)]
if i + 1 < M:
neighbor_list[3] = nodes[ids_2dto1d(i+1, j, M, N)]
if j - 1 >= 0:
neighbor_list[4] = nodes[ids_2dto1d(i, j-1, M, N)]
if i - 1 >= 0 and j - 1 >= 0:
neighbor_list[5] = nodes[ids_2dto1d(i-1, j-1, M, N)]
elif n == 4:
# square
if i - 1 >= 0:
neighbor_list[0] = nodes[ids_2dto1d(i-1, j, M, N)]
if j + 1 < N:
neighbor_list[1] = nodes[ids_2dto1d(i, j+1, M, N)]
if i + 1 < M:
neighbor_list[2] = nodes[ids_2dto1d(i+1, j, M, N)]
if j - 1 >= 0:
neighbor_list[3] = nodes[ids_2dto1d(i, j-1, M, N)]
return neighbor_list
def get_neighbor_index(i, j):
"""
1
6 2
center
5 3
4
return index of neighbor 1, 2, 3, 4, 5,6 in the matrix
"""
neighbor_matrix_ids = []
if j % 2 == 0:
neighbor_matrix_ids = [[i - 1, j ],
[i, j + 1],
[i + 1, j + 1],
[i + 1, j ],
[i + 1, j - 1],
[i , j - 1]]
elif j % 2 == 1:
neighbor_matrix_ids = [[i - 1, j ],
[i - 1, j + 1],
[i , j + 1],
[i + 1, j ],
[i , j - 1],
[i - 1, j - 1]]
return neighbor_matrix_ids
def get_layers_neighbors(i, j, l_max, M, N):
"""get neighbors of node layer by layer, todo BFS.
i, j: center node location
L_max: max number of layers
layers_neighbors: layers_neighbors[0] first layer neighbor: 6 nodes: can arrived in 1 time step.
layers_neighbors[1]: 2nd layer nodes id
M, N: matrix rows and columns.
"""
assert l_max >= 1
layers_neighbors = []
layer1_neighbor = get_neighbor_index(i, j) #[[1,1], [0, 1], ...]
temp = []
for item in layer1_neighbor:
x, y = item
if 0 <= x <= M-1 and 0 <= y <= N-1:
temp.append(item)
layers_neighbors.append(temp)
node_id_neighbors = []
for item in layer1_neighbor:
x, y = item
if 0 <= x <= M-1 and 0 <= y <= N-1:
node_id_neighbors.append(ids_2dto1d(x, y, M, N))
layers_neighbors_set = set(node_id_neighbors)
curr_ndoe_id = ids_2dto1d(i, j, M, N)
layers_neighbors_set.add(curr_ndoe_id)
t = 1
while t < l_max:
t += 1
layer_neighbor_temp = []
for item in layers_neighbors[-1]:
x, y = item
if 0 <= x <= M-1 and 0 <= y <= N-1:
layer_neighbor_temp += get_neighbor_index(x, y)
layer_neighbor = [] # remove previous layer neighbors
for item in layer_neighbor_temp:
x, y = item
if 0 <= x <= M-1 and 0 <= y <= N-1:
node_id = ids_2dto1d(x, y, M, N)
if node_id not in layers_neighbors_set:
layer_neighbor.append(item)
layers_neighbors_set.add(node_id)
layers_neighbors.append(layer_neighbor)
return layers_neighbors
def get_driver_status(env):
idle_driver_dist = np.zeros((env.M, env.N))
for driver_id, cur_drivers in env.drivers.iteritems():
if cur_drivers.node is not None:
node_id = cur_drivers.node.get_node_index()
row, col = ids_1dto2d(node_id, env.M, env.N)
if cur_drivers.onservice is False and cur_drivers.online is True:
idle_driver_dist[row, col] += 1
return idle_driver_dist
def debug_print_drivers(node):
print("Status of all drivers in the node {}".format(node.get_node_index()))
print("|{:12}|{:12}|{:12}|{:12}|".format("driver id", "driver location", "online", "onservice"))
for driver_id, cur_drivers in node.drivers.iteritems():
if cur_drivers.node is not None:
node_id = cur_drivers.node.get_node_index()
else:
node_id = "none"
print("|{:12}|{:12}|{:12}|{:12}|".format(driver_id, node_id, cur_drivers.online, cur_drivers.onservice))
================================================
FILE: tests/run_example.py
================================================
from collections import defaultdict
import sys
import traceback
import os, sys
sys.path.append("../")
from simulator.envs import *
def running_example():
mapped_matrix_int = np.array([[1, -100, 3], [5, 4, 2], [6, 7, 8]])
M, N = mapped_matrix_int.shape
order_num_dist = []
num_valid_grid = 8
idle_driver_location_mat = np.zeros((144, 8))
for ii in np.arange(144):
time_dict = {}
for jj in np.arange(M*N): # num of grids
time_dict[jj] = [2]
order_num_dist.append(time_dict)
idle_driver_location_mat[ii, :] = [2] * num_valid_grid
idle_driver_dist_time = [[10, 1] for _ in np.arange(144)]
n_side = 6
l_max = 2
order_time = [0.2, 0.2, 0.15,
0.15, 0.1, 0.1,
0.05, 0.04, 0.01]
order_price = [[10.17, 3.34], # mean and std of order price when duration is 10 min
[15.02, 6.90], # mean and std of order price when duration is 20 min
[23.22, 11.63],
[32.14, 16.20],
[40.99, 20.69],
[49.94, 25.61],
[58.98, 31.69],
[68.80, 37.25],
[79.40, 44.39]]
order_real = []
onoff_driver_location_mat = []
for tt in np.arange(144):
order_real += [[1, 5, tt, 1, 13.2], [2, 6, tt, 1, 13.2], [3, 1, tt, 1, 13.2],
[5, 3, tt, 1, 13.2], [6, 2, tt, 1, 13.2], [7, 9, tt, 1, 13.2],
[9, 10, tt, 1, 13.2], [10, 6, tt, 1, 13.2], [9, 7, tt, 1, 13.2]]
onoff_driver_location_mat.append([[-0.625, 2.92350389],
[0.09090909, 1.46398452],
[0.09090909, 2.36596622],
[0.09090909, 2.36596622],
[0.09090909, 1.46398452],
[0.09090909, 1.46398452],
[0.09090909, 1.46398452],
[0.09090909, 1.46398452],
[0.09090909, 1.46398452]])
env = CityReal(mapped_matrix_int, order_num_dist, idle_driver_dist_time, idle_driver_location_mat,
order_time, order_price, l_max, M, N, n_side, 1, np.array(order_real), np.array(onoff_driver_location_mat))
state = env.reset_clean()
order_response_rates = []
T = 0
max_iter = 144
while T < max_iter:
# if T % 5 == 0:
# state = env.reset_clean(generate_order=2)
dispatch_action = []
state, reward, _ = env.step(dispatch_action, generate_order=2)
print "City time {}: Order response rate: {}".format(env.city_time-1, env.order_response_rate)
order_response_rates.append(env.order_response_rate)
print("idle driver: {} == {} total num of drivers: {}".format(np.sum(state[0]),
np.sum(env.get_observation_driver_state()),
len(env.drivers.keys())))
assert np.sum(state[0]) == env.n_drivers
T += 1
print np.mean(order_response_rates)
if __name__ == "__main__":
running_example()
gitextract_vka5t1u7/
├── .gitignore
├── LICENSE
├── README.md
├── algorithm/
│ ├── IDQN.py
│ ├── __init__.py
│ ├── alg_utility.py
│ ├── cA2C.py
│ └── cDQN.py
├── run/
│ ├── run_IDQN.py
│ ├── run_baseline_nopolicy.py
│ ├── run_cA2C.py
│ └── run_cDQN.py
├── simulator/
│ ├── __init__.py
│ ├── envs.py
│ ├── objects.py
│ └── utilities.py
└── tests/
└── run_example.py
SYMBOL INDEX (217 symbols across 9 files)
FILE: algorithm/IDQN.py
class Estimator (line 8) | class Estimator:
method __init__ (line 11) | def __init__(self,
method _build_model (line 65) | def _build_model(self):
method predict (line 100) | def predict(self, s):
method action (line 105) | def action(self, s, context, epsilon):
method update (line 158) | def update(self, s, a, y, learning_rate, global_step):
class stateProcessor (line 180) | class stateProcessor:
method __init__ (line 185) | def __init__(self,
method utility_conver_states (line 196) | def utility_conver_states(self, curr_state):
method utility_conver_reward (line 201) | def utility_conver_reward(self, reward_node):
method reward_wrapper (line 205) | def reward_wrapper(self, info, curr_s):
method compute_context (line 219) | def compute_context(self, info):
method utility_normalize_states (line 225) | def utility_normalize_states(self, curr_s):
method to_grid_states (line 235) | def to_grid_states(self, curr_s, curr_city_time):
method to_grid_rewards (line 249) | def to_grid_rewards(self, action_idx_valid, node_reward):
method to_grid_next_states (line 257) | def to_grid_next_states(self, s_grid, next_state, action_index, curr_c...
method to_grid_state_for_training (line 278) | def to_grid_state_for_training(self, s_grid, action_starting_gridids):
method to_action_mat (line 285) | def to_action_mat(self, action_neighbor_idx):
class ReplayMemory (line 292) | class ReplayMemory:
method __init__ (line 296) | def __init__(self, memory_size, batch_size):
method add (line 307) | def add(self, s, a, r, next_s):
method sample (line 330) | def sample(self):
method reset (line 341) | def reset(self):
class ModelParametersCopier (line 349) | class ModelParametersCopier():
method __init__ (line 354) | def __init__(self, estimator1, estimator2):
method make (line 371) | def make(self, sess):
FILE: algorithm/alg_utility.py
function tfsum (line 8) | def tfsum(x, axis=None, keepdims=False):
class Pd (line 12) | class Pd(object):
method mode (line 17) | def mode(self):
method neglogp (line 20) | def neglogp(self, x):
method kl (line 24) | def kl(self, other):
method entropy (line 27) | def entropy(self):
method sample (line 30) | def sample(self):
method logp (line 33) | def logp(self, x):
class DiagGaussianPd (line 37) | class DiagGaussianPd(Pd):
method __init__ (line 38) | def __init__(self, mu, logstd):
method mode (line 43) | def mode(self):
method neglogp (line 46) | def neglogp(self, x):
method sample (line 52) | def sample(self):
function normalize_reward (line 57) | def normalize_reward(discounted_epr):
function continuous_quadratic_knapsack (line 76) | def continuous_quadratic_knapsack(b, u, r):
function projection_fast (line 131) | def projection_fast(u, n, num_idle_driver):
function categorical_sample_split (line 138) | def categorical_sample_split(logits, d=6):
function fc (line 150) | def fc(x, scope, nh, act=tf.nn.relu, init_scale=1.0):
function ortho_init (line 161) | def ortho_init(scale=1.0):
function get_target_ids_local (line 184) | def get_target_ids_local(mapped_matrix_int_local):
function collision_action (line 194) | def collision_action(action_tuple):
function construct_grid_nodeid_mapping (line 207) | def construct_grid_nodeid_mapping(target_ids_local, grid_ids_local):
function utility_conver_states (line 215) | def utility_conver_states(curr_s, target_id_states):
function utility_conver_reward (line 219) | def utility_conver_reward(reward_node, target_id_states):
function compute_sum_qtable (line 226) | def compute_sum_qtable(temp_qtable):
FILE: algorithm/cA2C.py
class Estimator (line 9) | class Estimator:
method __init__ (line 12) | def __init__(self,
method _build_value_model (line 90) | def _build_value_model(self):
method _build_mlp_policy (line 115) | def _build_mlp_policy(self):
method predict (line 142) | def predict(self, s):
method action (line 147) | def action(self, s, context, epsilon):
method compute_advantage (line 222) | def compute_advantage(self, curr_state_value, next_state_ids, next_sta...
method compute_targets (line 234) | def compute_targets(self, valid_prob, next_state, node_reward, gamma):
method initialization (line 248) | def initialization(self, s, y, learning_rate):
method update_policy (line 254) | def update_policy(self, policy_state, advantage, action_choosen_mat, c...
method update_value (line 268) | def update_value(self, s, y, learning_rate, global_step):
class stateProcessor (line 292) | class stateProcessor:
method __init__ (line 297) | def __init__(self,
method utility_conver_states (line 308) | def utility_conver_states(self, curr_state):
method utility_normalize_states (line 313) | def utility_normalize_states(self, curr_s):
method utility_conver_reward (line 325) | def utility_conver_reward(self, reward_node):
method reward_wrapper (line 329) | def reward_wrapper(self, info, curr_s):
method compute_context (line 343) | def compute_context(self, info):
method to_grid_states (line 349) | def to_grid_states(self, curr_s, curr_city_time):
method to_grid_rewards (line 370) | def to_grid_rewards(self, node_reward):
method to_action_mat (line 377) | def to_action_mat(self, action_neighbor_idx):
class policyReplayMemory (line 383) | class policyReplayMemory:
method __init__ (line 384) | def __init__(self, memory_size, batch_size):
method add (line 396) | def add(self, s, a, r, mask):
method sample (line 420) | def sample(self):
method reset (line 432) | def reset(self):
class ReplayMemory (line 440) | class ReplayMemory:
method __init__ (line 444) | def __init__(self, memory_size, batch_size):
method add (line 455) | def add(self, s, a, r, next_s):
method sample (line 479) | def sample(self):
method reset (line 491) | def reset(self):
class ModelParametersCopier (line 500) | class ModelParametersCopier():
method __init__ (line 505) | def __init__(self, estimator1, estimator2):
method make (line 522) | def make(self, sess):
FILE: algorithm/cDQN.py
class Estimator (line 9) | class Estimator:
method __init__ (line 12) | def __init__(self,
method _build_model (line 74) | def _build_model(self):
method predict (line 98) | def predict(self, s):
method action (line 103) | def action(self, s, context, epsilon):
method compute_targets (line 153) | def compute_targets(self, valid_prob, next_state, node_reward, gamma):
method initialization (line 167) | def initialization(self, s, y, learning_rate):
method update (line 173) | def update(self, s, y, learning_rate, global_step):
method _build_cnn_model (line 194) | def _build_cnn_model(self):
class stateProcessor (line 220) | class stateProcessor:
method __init__ (line 225) | def __init__(self,
method utility_conver_states (line 236) | def utility_conver_states(self, curr_state):
method utility_normalize_states (line 241) | def utility_normalize_states(self, curr_s):
method utility_conver_reward (line 250) | def utility_conver_reward(self, reward_node):
method reward_wrapper (line 254) | def reward_wrapper(self, info, curr_s):
method compute_context (line 262) | def compute_context(self, info):
method to_grid_states (line 268) | def to_grid_states(self, curr_s, curr_city_time):
method to_grid_rewards (line 284) | def to_grid_rewards(self, node_reward):
method to_action_mat (line 287) | def to_action_mat(self, action_neighbor_idx):
class ReplayMemory (line 293) | class ReplayMemory:
method __init__ (line 297) | def __init__(self, memory_size, batch_size):
method add (line 308) | def add(self, s, a, r, next_s):
method sample (line 331) | def sample(self):
method reset (line 342) | def reset(self):
class ModelParametersCopier (line 351) | class ModelParametersCopier():
method __init__ (line 356) | def __init__(self, estimator1, estimator2):
method make (line 373) | def make(self, sess):
FILE: run/run_baseline_nopolicy.py
function compute_context (line 80) | def compute_context(target_grids, info):
FILE: simulator/envs.py
class CityReal (line 24) | class CityReal:
method __init__ (line 27) | def __init__(self, mapped_matrix_int, order_num_dist, idle_driver_dist...
method construct_map_simulation (line 123) | def construct_map_simulation(self, M, N, n):
method construct_mapping (line 134) | def construct_mapping(self):
method construct_node_real (line 142) | def construct_node_real(self, mapped_matrix_int):
method construct_map_real (line 160) | def construct_map_real(self, n_side):
method initial_order_random (line 168) | def initial_order_random(self, distribution_all, dis_paras_all):
method get_observation (line 177) | def get_observation(self):
method get_num_idle_drivers (line 187) | def get_num_idle_drivers(self):
method get_observation_driver_state (line 197) | def get_observation_driver_state(self):
method reset_randomseed (line 208) | def reset_randomseed(self, random_seed):
method reset (line 211) | def reset(self):
method reset_clean (line 242) | def reset_clean(self, generate_order=1, ratio=1, city_time=""):
method utility_collect_offline_drivers_id (line 276) | def utility_collect_offline_drivers_id(self):
method utility_get_n_idle_drivers_nodewise (line 288) | def utility_get_n_idle_drivers_nodewise(self):
method utility_add_driver_real_new (line 297) | def utility_add_driver_real_new(self, num_added_driver):
method utility_add_driver_real_new_offlinefirst (line 320) | def utility_add_driver_real_new_offlinefirst(self, num_added_driver):
method utility_add_driver_real_nodewise (line 353) | def utility_add_driver_real_nodewise(self, node_id, num_added_driver):
method utility_set_drivers_offline_real_nodewise (line 371) | def utility_set_drivers_offline_real_nodewise(self, node_id, n_drivers...
method utility_set_drivers_offline_real_new (line 383) | def utility_set_drivers_offline_real_new(self, n_drivers_to_off):
method utility_bootstrap_oneday_order (line 417) | def utility_bootstrap_oneday_order(self):
method step_driver_status_control (line 445) | def step_driver_status_control(self):
method step_driver_online_offline_nodewise (line 456) | def step_driver_online_offline_nodewise(self):
method step_driver_online_offline_control_new (line 478) | def step_driver_online_offline_control_new(self, n_idle_drivers):
method step_driver_online_offline_control (line 497) | def step_driver_online_offline_control(self, n_idle_drivers):
method utility_get_n_idle_drivers_real (line 524) | def utility_get_n_idle_drivers_real(self):
method utility_set_neighbor_weight (line 533) | def utility_set_neighbor_weight(self, weights):
method step_generate_order_real (line 536) | def step_generate_order_real(self):
method step_bootstrap_order_real (line 546) | def step_bootstrap_order_real(self, day_orders_t):
method step_assign_order (line 558) | def step_assign_order(self):
method step_assign_order_broadcast_neighbor_reward_update (line 576) | def step_assign_order_broadcast_neighbor_reward_update(self):
method step_remove_unfinished_orders (line 614) | def step_remove_unfinished_orders(self):
method step_pre_order_assigin (line 619) | def step_pre_order_assigin(self, next_state):
method step_dispatch_invalid (line 652) | def step_dispatch_invalid(self, dispatch_actions):
method step_add_dispatched_drivers (line 697) | def step_add_dispatched_drivers(self, save_remove_id):
method step_increase_city_time (line 705) | def step_increase_city_time(self):
method step (line 711) | def step(self, dispatch_actions, generate_order=1):
FILE: simulator/objects.py
class Distribution (line 5) | class Distribution():
method sample (line 9) | def sample(self):
class PoissonDistribution (line 12) | class PoissonDistribution(Distribution):
method __init__ (line 14) | def __init__(self, lam):
method sample (line 17) | def sample(self, seed=0):
class GaussianDistribution (line 22) | class GaussianDistribution(Distribution):
method __init__ (line 24) | def __init__(self, args):
method sample (line 29) | def sample(self, seed=0):
class Node (line 34) | class Node(object):
method __init__ (line 40) | def __init__(self, index):
method clean_node (line 58) | def clean_node(self):
method get_layers_neighbors (line 65) | def get_layers_neighbors(self, l_max, M, N, env):
method get_node_index (line 78) | def get_node_index(self):
method get_driver_numbers (line 81) | def get_driver_numbers(self):
method get_idle_driver_numbers_loop (line 84) | def get_idle_driver_numbers_loop(self):
method get_off_driver_numbers_loop (line 91) | def get_off_driver_numbers_loop(self):
method order_distribution (line 98) | def order_distribution(self, distribution, dis_paras):
method generate_order_random (line 107) | def generate_order_random(self, city_time, nodes, seed):
method generate_order_real (line 127) | def generate_order_real(self, l_max, order_time_dist, order_price_dist...
method add_order_real (line 156) | def add_order_real(self, city_time, destination_node, duration, price):
method set_neighbors (line 165) | def set_neighbors(self, nodes_list):
method remove_idle_driver_random (line 169) | def remove_idle_driver_random(self):
method set_idle_driver_offline_random (line 181) | def set_idle_driver_offline_random(self):
method set_offline_driver_online (line 193) | def set_offline_driver_online(self):
method get_driver_random (line 205) | def get_driver_random(self):
method remove_driver (line 214) | def remove_driver(self, driver_id):
method add_driver (line 223) | def add_driver(self, driver_id, driver):
method remove_unfinished_order (line 227) | def remove_unfinished_order(self, city_time):
method simple_order_assign (line 243) | def simple_order_assign(self, city_time, city):
method simple_order_assign_real (line 270) | def simple_order_assign_real(self, city_time, city):
method simple_order_assign_broadcast_update (line 302) | def simple_order_assign_broadcast_update(self, city, neighbor_node_rew...
method utility_assign_orders_neighbor (line 320) | def utility_assign_orders_neighbor(self, city, neighbor_node, num_assi...
class Driver (line 349) | class Driver(object):
method __init__ (line 352) | def __init__(self, driver_id):
method set_position (line 362) | def set_position(self, node):
method set_order_start (line 365) | def set_order_start(self, order):
method set_order_finish (line 368) | def set_order_finish(self):
method get_driver_id (line 372) | def get_driver_id(self):
method update_city_time (line 375) | def update_city_time(self):
method set_city_time (line 378) | def set_city_time(self, city_time):
method set_offline (line 381) | def set_offline(self):
method set_offline_for_start_dispatch (line 387) | def set_offline_for_start_dispatch(self):
method set_online (line 392) | def set_online(self):
method set_online_for_finish_dispatch (line 398) | def set_online_for_finish_dispatch(self):
method take_order (line 403) | def take_order(self, order):
method status_control_eachtime (line 411) | def status_control_eachtime(self, city):
class Order (line 428) | class Order(object):
method __init__ (line 432) | def __init__(self, begin_position, end_position, begin_time, duration,...
method get_begin_position (line 442) | def get_begin_position(self):
method get_begin_position_id (line 445) | def get_begin_position_id(self):
method get_end_position (line 448) | def get_end_position(self):
method get_begin_time (line 451) | def get_begin_time(self):
method set_assigned_time (line 454) | def set_assigned_time(self, city_time):
method get_assigned_time (line 457) | def get_assigned_time(self):
method get_duration (line 463) | def get_duration(self):
method get_price (line 466) | def get_price(self):
method get_wait_time (line 469) | def get_wait_time(self):
FILE: simulator/utilities.py
function datetime_range (line 9) | def datetime_range(start, end, delta):
function mkdir_p (line 16) | def mkdir_p(path):
function ids_2dto1d (line 26) | def ids_2dto1d(i, j, M, N):
function ids_1dto2d (line 38) | def ids_1dto2d(ids, M, N):
function get_neighbor_list (line 47) | def get_neighbor_list(i, j, M, N, n, nodes):
function get_neighbor_index (line 100) | def get_neighbor_index(i, j):
function get_layers_neighbors (line 128) | def get_layers_neighbors(i, j, l_max, M, N):
function get_driver_status (line 178) | def get_driver_status(env):
function debug_print_drivers (line 189) | def debug_print_drivers(node):
FILE: tests/run_example.py
function running_example (line 10) | def running_example():
Condensed preview — 17 files, each showing path, character count, and a content snippet. Download the .json file or copy for the full structured content (192K chars).
[
{
"path": ".gitignore",
"chars": 1621,
"preview": "## Core latex/pdflatex auxiliary files:\n*.aux\n*.lof\n*.log\n*.lot\n*.fls\n*.out\n*.toc\n*.fmt\n\n# python scripts\n*DS_Store*\n*.i"
},
{
"path": "LICENSE",
"chars": 35147,
"preview": " GNU GENERAL PUBLIC LICENSE\n Version 3, 29 June 2007\n\n Copyright (C) 2007 Free "
},
{
"path": "README.md",
"chars": 884,
"preview": "# Simulator\nThis simulator serves as the training and evaluation platform in the following work:\n\n\n> Efficient Large-Sca"
},
{
"path": "algorithm/IDQN.py",
"chars": 14809,
"preview": "\nimport numpy as np\nimport tensorflow as tf\nimport random, os\nfrom alg_utility import *\n\n\nclass Estimator:\n \"\"\" build"
},
{
"path": "algorithm/__init__.py",
"chars": 0,
"preview": ""
},
{
"path": "algorithm/alg_utility.py",
"chars": 7013,
"preview": "import tensorflow as tf\nimport numpy as np\n# import cvxpy as cvx\nfrom simulator.utilities import *\n\n\"\"\" Some of Followin"
},
{
"path": "algorithm/cA2C.py",
"chars": 21564,
"preview": "\n\nimport numpy as np\nimport tensorflow as tf\nimport random, os\nfrom alg_utility import *\nfrom copy import deepcopy\n\nclas"
},
{
"path": "algorithm/cDQN.py",
"chars": 14987,
"preview": "\n\nimport numpy as np\nimport tensorflow as tf\nimport random, os\nfrom alg_utility import *\n\n# this is essentially deep exp"
},
{
"path": "run/run_IDQN.py",
"chars": 8331,
"preview": "import pickle, sys\nsys.path.append(\"../\")\n\n# from simulator.utilities import *\nfrom algorithm.alg_utility import *\nfrom "
},
{
"path": "run/run_baseline_nopolicy.py",
"chars": 5337,
"preview": "\nimport pickle, sys\nsys.path.append(\"../\")\n\nfrom simulator.envs import *\n\n\n################## Load data ################"
},
{
"path": "run/run_cA2C.py",
"chars": 9032,
"preview": "# -*- coding: utf-8 -*-\nimport pickle, sys\nsys.path.append(\"../\")\n\n# from simulator.utilities import *\nfrom algorithm.al"
},
{
"path": "run/run_cDQN.py",
"chars": 8275,
"preview": "\nimport pickle, sys\nsys.path.append(\"../\")\n\n# from simulator.utilities import *\nfrom algorithm.alg_utility import *\nfrom"
},
{
"path": "simulator/__init__.py",
"chars": 3,
"preview": "\n\n\n"
},
{
"path": "simulator/envs.py",
"chars": 31824,
"preview": "import os, sys, random, time\nimport logging\nsys.path.append(\"../\")\n\nfrom objects import *\nfrom utilities import *\n# from"
},
{
"path": "simulator/objects.py",
"chars": 17296,
"preview": "import numpy as np\nfrom abc import ABCMeta, abstractmethod\nfrom utilities import *\n\nclass Distribution():\n ''' Define"
},
{
"path": "simulator/utilities.py",
"chars": 6424,
"preview": "import numpy as np\nimport os\nimport errno\n\n\n\nfrom datetime import datetime, timedelta\n\ndef datetime_range(start, end, de"
},
{
"path": "tests/run_example.py",
"chars": 3325,
"preview": "\nfrom collections import defaultdict\nimport sys\nimport traceback\nimport os, sys\nsys.path.append(\"../\")\nfrom simulator.en"
}
]
About this extraction
This page contains the full source code of the illidanlab/Simulator GitHub repository, extracted and formatted as plain text for AI agents and large language models (LLMs). The extraction includes 17 files (181.5 KB), approximately 43.6k tokens, and a symbol index with 217 extracted functions, classes, methods, constants, and types. Use this with OpenClaw, Claude, ChatGPT, Cursor, Windsurf, or any other AI tool that accepts text input. You can copy the full output to your clipboard or download it as a .txt file.
Extracted by GitExtract — free GitHub repo to text converter for AI. Built by Nikandr Surkov.