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DQN.py
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DQN.py
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# DQN
import random
import numpy as np
import cv2
import PIL
from keras.models import Model
from keras.layers import Conv2D, MaxPooling2D, Dense, Lambda, Flatten, Input, average, Add
from keras.backend import repeat_elements
from keras.optimizers import Adam
import keras
import tensorflow as tf
from collections import deque
class DQNAgent:
def __init__(self, inp_action_size):
self.action_size = inp_action_size
self.name="DDDQN"
self.memory = deque(maxlen=2000)
self.gamma = 0.95
self.epsilon = 0.6
self.epsilon_decay = 0.98
self.epsilon_min = 0.01
self.learning_rate = 0.0005
self.tau = 200
self.tau_counter = 0
self.DQNetwork = self._build_model(self.name+"-l")
self.TargetNetwork = self._build_model(self.name+"-t")
self.sync_networks(0)
# self.model = self._build_model()
self.store_list = []
def _build_model(self,name, input_shape=(460, 460, 1)):
h, w, d=input_shape
# Constructing a Duelling Deep Q-Network
# model = Sequential()
# model.add(Lambda(lambda x: x / 127.5 - 1., input_shape=input_shape, output_shape=input_shape))
# model.add(Conv2D(32, (3, 3), activation='relu', name='conv1', input_shape=input_shape, padding='same'))
# model.add(MaxPooling2D(pool_size=(2, 2)))
# model.add(Conv2D(64, (4, 4), activation='relu', name='conv2', padding='same', strides=(2, 2)))
# model.add(MaxPooling2D(pool_size=(2, 2)))
# model.add(Flatten())
# model.add(Dense(256, activation='relu'))
# model.add(Dense(2, activation='linear'))
# inputs = Input(shape=input_shape)
# x = Lambda(lambda x: x / 127.5 - 1, input_shape=input_shape, output_shape=input_shape)(inputs)
# x = Conv2D(32, (3, 3), activation='relu', name='conv1', padding='same', input_shape=input_shape)(x)
# x = MaxPooling2D(pool_size=(2, 2))(x)
# x = Conv2D(64, (4, 4), activation='relu', name='conv2', padding='same')(x)
# x = MaxPooling2D(pool_size=(2, 2))(x)
# x = Flatten()(x)
#
# value = Dense(1, activation='relu')(x)
# action_advantage = Dense(self.action_size, activation='relu')(x)
# added=keras.layers.add([action_advantage, action_advantage])
# # averaged=keras.layers.average([action_advantage, action_advantage])
# # multiplied=repeat_elements(value, self.action_size, 0)
# average_action_advantage = keras.backend.mean(action_advantage, axis=[-1], keepdims=True)
# keras.layers.average(action_advantage)
# # k=repeat_elements(value, self.action_size, -1)
# # Q_values = Add()([repeat_elements(value, self.action_size, -1), average_action_advantage])
#
# model = Model(inputs, average_action_advantage)
# model.compile(loss='mse', optimizer=Adam(lr=self.learning_rate))
#
# model.summary()
#
# return model
# We use tf.variable_scope here to know which network we're using (DQN or target_net)
# it will be useful when we will update our w- parameters (by copy the DQN parameters)
with tf.variable_scope(name):
# We create the placeholders
# *state_size means that we take each elements of state_size in tuple hence is like if we wrote
# [None, 100, 120, 4]
self.inputs_ = tf.placeholder(tf.float32, [None, h,w,d], name="inputs")
#
self.ISWeights_ = tf.placeholder(tf.float32, [None, 1], name='IS_weights')
self.actions_ = tf.placeholder(tf.float32, [None, self.action_size], name="actions_")
# Remember that target_Q is the R(s,a) + ymax Qhat(s', a')
self.target_Q = tf.placeholder(tf.float32, [None], name="target")
"""
First convnet:
CNN
ELU
"""
# Input is 100x120x4
self.conv1 = tf.layers.conv2d(inputs=self.inputs_,
filters=32,
kernel_size=[8, 8],
strides=[4, 4],
padding="VALID",
kernel_initializer=tf.contrib.layers.xavier_initializer_conv2d(),
name="conv1")
self.conv1_out = tf.nn.elu(self.conv1, name="conv1_out")
"""
Second convnet:
CNN
ELU
"""
self.conv2 = tf.layers.conv2d(inputs=self.conv1_out,
filters=64,
kernel_size=[4, 4],
strides=[2, 2],
padding="VALID",
kernel_initializer=tf.contrib.layers.xavier_initializer_conv2d(),
name="conv2")
self.conv2_out = tf.nn.elu(self.conv2, name="conv2_out")
"""
Third convnet:
CNN
ELU
"""
self.conv3 = tf.layers.conv2d(inputs=self.conv2_out,
filters=128,
kernel_size=[4, 4],
strides=[2, 2],
padding="VALID",
kernel_initializer=tf.contrib.layers.xavier_initializer_conv2d(),
name="conv3")
self.conv3_out = tf.nn.elu(self.conv3, name="conv3_out")
self.flatten = tf.layers.flatten(self.conv3_out)
## Here we separate into two streams
# The one that calculate V(s)
self.value_fc = tf.layers.dense(inputs=self.flatten,
units=512,
activation=tf.nn.elu,
kernel_initializer=tf.contrib.layers.xavier_initializer(),
name="value_fc")
self.value = tf.layers.dense(inputs=self.value_fc,
units=1,
activation=None,
kernel_initializer=tf.contrib.layers.xavier_initializer(),
name="value")
# The one that calculate A(s,a)
self.advantage_fc = tf.layers.dense(inputs=self.flatten,
units=512,
activation=tf.nn.elu,
kernel_initializer=tf.contrib.layers.xavier_initializer(),
name="advantage_fc")
self.advantage = tf.layers.dense(inputs=self.advantage_fc,
units=self.action_size,
activation=None,
kernel_initializer=tf.contrib.layers.xavier_initializer(),
name="advantages")
# Agregating layer
# Q(s,a) = V(s) + (A(s,a) - 1/|A| * sum A(s,a'))
self.output = self.value + tf.subtract(self.advantage,
tf.reduce_mean(self.advantage, axis=1, keepdims=True))
# Q is our predicted Q value.
self.Q = tf.reduce_sum(tf.multiply(self.output, self.actions_), axis=1)
# The loss is modified because of PER
self.absolute_errors = tf.abs(self.target_Q - self.Q) # for updating Sumtree
self.loss = tf.reduce_mean(self.ISWeights_ * tf.squared_difference(self.target_Q, self.Q))
self.optimizer = tf.train.RMSPropOptimizer(self.learning_rate).minimize(self.loss)
return tf.get_default_graph()
# The weights from the target network are updated to the DQN-weights
def sync_networks(self, sync_num):
name = "Models/model_sync_{}.h5".format(sync_num)
self.save(name, self.DQNetwork)
self.load(name, self.TargetNetwork)
def pre_process(self, image):
image = np.array(image)
# print('image', image)
gray = cv2.cvtColor(image[30:490, 0:460], cv2.COLOR_BGR2GRAY)
gray = gray.reshape(1, 460, 460, 1)
# gray1=gray.reshape(460, 460)
# PIL.Image.fromarray(gray1).show()
return gray
def _remember(self, state, action, reward, next_state, done):
self.memory.append((state, action, reward, next_state, done))
def _store(self, state, action, reward, done):
self.store_list.append([state, action, reward, done])
# def store_to_remember(self):
# self.store_list[-1][3] = True
# self.store_list[-1][2] = -10
# for x in range(len(self.store_list) - 1):
# elem = self.store_list[x]
# next_elem = self.store_list[x + 1]
# self._remember(elem[0], elem[1], next_elem[2], next_elem[0], next_elem[3])
# self.store_list = []
# def act_store(self, state, reward, done):
# state = self.pre_process(state)
# action = self.act(state)
# self._store(state, action, reward, done)
# return action
def predict(self, state):
return self.TargetNetwork.predict(state)
def act(self, state, pre_process=False):
if pre_process:
state = self.pre_process(state)
if np.random.rand() <= self.epsilon:
return random.randrange(self.action_size)
act_values = self.TargetNetwork.predict(state)
return np.argmax(act_values[0])
def replay(self, batch_size, minibatch):
# minibatch = random.sample(self.memory, batch_size)
state, action, reward, next_state, done = minibatch
for state, action, reward, next_state, done in minibatch:
self.tau_counter += 1
# Updating target network - part of fixed Q-targets
if self.tau_counter == self.tau:
self.tau_counter = 0
self.sync_networks(random.randint(0, 100000000))
target = reward
if not done:
print('pred', self.TargetNetwork.predict(next_state))
# Double learning
target_act = np.argmax(self.DQNetwork.predict(next_state))
target = reward + self.gamma * self.TargetNetwork.predict(next_state)[0][target_act]
print('target', target, self.epsilon)
target_f = self.DQNetwork.predict(state)
target_f[0][action] = target
train_history = self.DQNetwork.fit(state, target_f, epochs=1, verbose=0)
if self.epsilon > self.epsilon_min:
self.epsilon *= self.epsilon_decay
def load(self, name, model):
model.load_weights(name)
def save(self, name, model):
with tf.Session() as sess:
sess.run(model)
tf.train.Saver().save(sess, name)