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DQN_Modified.py
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DQN_Modified.py
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"""
This section of code is the DQN's brain————Brain of the agent.
All decisions are made here.
Using Tensorflow:1.4 to build the neural network.
@file DQN_Modified.py
@author Haoyi Niu
@date 2020-06-21
"""
import numpy as np
import pandas as pd
import tensorflow as tf
import keras.backend.tensorflow_backend as KTF
import keras as K
from keras.layers import Permute, Dense, Lambda, RepeatVector, Multiply
import os
np.random.seed(1)
tf.set_random_seed(1)
# fix a proportion or threshold for GPU"0" usage
os.environ["CUDA_VISIBLE_DEVICES"] = "0"
config = tf.ConfigProto()
config.gpu_options.allow_growth=True # allocate fraction of GPU memory by needs
sess = tf.Session(config=config)
KTF.set_session(sess)
# Deep Q Network off-policy
class DeepQNetwork:
def __init__(
self,
n_actions,
n_features,
learning_rate=0.01,
reward_decay=0.9,
e_greedy=0.9,
replace_target_iter=300,
memory_size=500,
batch_size=32,
e_greedy_increment=None,
output_graph=False,
):
self.n_actions = n_actions
self.n_features = n_features
self.lr = learning_rate
self.gamma = reward_decay
self.epsilon_max = e_greedy
self.replace_target_iter = replace_target_iter
self.memory_size = memory_size
self.batch_size = batch_size
self.epsilon_increment = e_greedy_increment
self.epsilon = 0 if e_greedy_increment is not None else self.epsilon_max
# total learning step
self.learn_step_counter = 0
# initialize zero memory [s, a, r, s_]
self.memory = np.zeros((self.memory_size, n_features * 2 + 2))
# consist of [target_net, evaluate_net]
self._build_net()
t_params = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, scope='target_net')
e_params = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, scope='eval_net')
with tf.variable_scope('hard_replacement'):
self.target_replace_op = [tf.assign(t, e) for t, e in zip(t_params, e_params)]
self.sess = tf.Session()
if output_graph:
# $ tensorboard --logdir=logs
tf.summary.FileWriter("logs/", self.sess.graph)
self.sess.run(tf.global_variables_initializer())
self.cost_his = []
def _build_net(self):
# ------------------ all inputs ------------------------
self.s = tf.placeholder(tf.float32, [None, self.n_features], name='s') # input State
self.s_ = tf.placeholder(tf.float32, [None, self.n_features], name='s_') # input Next State
self.r = tf.placeholder(tf.float32, [None, ], name='r') # input Reward
self.a = tf.placeholder(tf.int32, [None, ], name='a') # input Action
w_initializer, b_initializer = tf.random_normal_initializer(0., 0.3), tf.constant_initializer(0.1)
# ------------------ build evaluate_net ------------------
with tf.variable_scope('eval_net'):
e1 = tf.layers.dense(self.s, 20, tf.nn.relu, kernel_initializer=w_initializer,
bias_initializer=b_initializer, name='e1')
self.q_eval = tf.layers.dense(e1, self.n_actions, kernel_initializer=w_initializer,
bias_initializer=b_initializer, name='q')
# ------------------ build target_net ------------------
with tf.variable_scope('target_net'):
t1 = tf.layers.dense(self.s_, 20, tf.nn.relu, kernel_initializer=w_initializer,
bias_initializer=b_initializer, name='t1')
self.q_next = tf.layers.dense(t1, self.n_actions, kernel_initializer=w_initializer,
bias_initializer=b_initializer, name='t2')
with tf.variable_scope('q_target'):
q_target = self.r + self.gamma * tf.reduce_max(self.q_next, axis=1, name='Qmax_s_') # shape=(None, )
self.q_target = tf.stop_gradient(q_target)
with tf.variable_scope('q_eval'):
a_indices = tf.stack([tf.range(tf.shape(self.a)[0], dtype=tf.int32), self.a], axis=1)
self.q_eval_wrt_a = tf.gather_nd(params=self.q_eval, indices=a_indices) # shape=(None, )
with tf.variable_scope('loss'):
self.loss = tf.reduce_mean(tf.squared_difference(self.q_target, self.q_eval_wrt_a, name='TD_error'))
with tf.variable_scope('train'):
self._train_op = tf.train.RMSPropOptimizer(self.lr).minimize(self.loss)
def store_transition(self, s, a, r, s_):
if not hasattr(self, 'memory_counter'):
self.memory_counter = 0
transition = np.hstack((s, [a, r], s_))
# replace the old memory with new memory
index = self.memory_counter % self.memory_size
self.memory[index, :] = transition
self.memory_counter += 1
def choose_action(self, observation_list):
# to have batch dimension when feed into tf placeholder
observation = np.array(observation_list)
observation = observation[np.newaxis, :]
if np.random.uniform() < self.epsilon:
# forward feed the observation and get q value for every actions
actions_value = self.sess.run(self.q_eval, feed_dict={self.s: observation})
action = np.argmax(actions_value)
# Q-masking
actions_value_copy = actions_value
while (action == 2 and observation_list[0] <= 1) \
or (action == 3 and observation_list[0] <= 10) \
or (action == 6 and observation_list[1] <= 1)\
or (action == 7 and observation_list[1] <= 10):
actions_value_copy[0, np.argmax(actions_value_copy)] = np.min(actions_value)
action = np.argmax(actions_value_copy)
else:
while True:
action = np.random.randint(0, self.n_actions)
# Q-masking
if (action == 2 and observation_list[0] <= 1) \
or (action == 3 and observation_list[0] <= 10) \
or (action == 6 and observation_list[1] <= 1)\
or (action == 7 and observation_list[1] <= 10):
pass
else:
break
#print("----------action----------\n", action)
return action
def learn(self):
# check to replace target parameters
if self.learn_step_counter % self.replace_target_iter == 0:
self.sess.run(self.target_replace_op)
print('\ntarget_params_replaced\n')
# sample batch memory from all memory
if self.memory_counter > self.memory_size:
sample_index = np.random.choice(self.memory_size, size=self.batch_size)
else:
sample_index = np.random.choice(self.memory_counter, size=self.batch_size)
batch_memory = self.memory[sample_index, :]
_, cost = self.sess.run(
[self._train_op, self.loss],
feed_dict={
self.s: batch_memory[:, :self.n_features],
self.a: batch_memory[:, self.n_features],
self.r: batch_memory[:, self.n_features + 1],
self.s_: batch_memory[:, -self.n_features:],
})
self.cost_his.append(cost)
#print (self.cost_his)
# increasing epsilon
self.epsilon = self.epsilon + self.epsilon_increment if self.epsilon < self.epsilon_max else self.epsilon_max
self.learn_step_counter += 1
def plot_cost(self):
import matplotlib.pyplot as plt
plt.plot(np.arange(len(self.cost_his)), self.cost_his)
plt.ylabel('Cost')
plt.xlabel('training steps')
plt.show()
# if __name__ == '__main__':
# DQN = DeepQNetwork(3,4, output_graph=True)