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dqn2.py
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dqn2.py
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import tensorflow as tf
import numpy as np
import random
import time
import os
from collections import deque
from netutil import *
from game.flappy_bird import FlappyBird
INPUT_SIZE = 84
INPUT_CHANNEL = 4
ACTIONS_DIM = 2
GAMMA = 0.99
FINAL_EPSILON = 0.0001
INITIAL_EPSILON = 0.0001
ALPHA = 1e-6 # the learning rate of optimizer
MAX_TIME_STEP = 10 * 10 ** 7
EPSILON_TIME_STEP = 1 * 10 ** 6 # for annealing the epsilon greedy
EPSILON_ANNEAL = float(INITIAL_EPSILON - FINAL_EPSILON) / EPSILON_TIME_STEP
REPLAY_MEMORY = 50000
BATCH_SIZE = 32
CHECKPOINT_DIR = 'tmp_dqn2/checkpoints'
LOG_FILE = 'tmp_dqn2/log'
class DQN(object):
def __init__(self):
self.global_t = 0
self.replay_buffer = deque(maxlen=REPLAY_MEMORY)
# q-network parameter
self.create_network()
self.create_minimize()
# init session
self.session = tf.InteractiveSession()
self.session.run(tf.global_variables_initializer())
self.saver = tf.train.Saver(tf.global_variables())
self.restore()
self.epsilon = INITIAL_EPSILON - float(INITIAL_EPSILON - FINAL_EPSILON) \
* min(self.global_t, EPSILON_TIME_STEP) / float(EPSILON_TIME_STEP)
# for recording the log into tensorboard
self.time_input = tf.placeholder(tf.float32)
self.reward_input = tf.placeholder(tf.float32)
tf.summary.scalar('living_time', self.time_input)
tf.summary.scalar('reward', self.reward_input)
self.summary_op = tf.summary.merge_all()
self.summary_writer = tf.summary.FileWriter(LOG_FILE, self.session.graph)
self.episode_start_time = 0.0
self.episode_reward = 0.0
return
def create_network(self):
# input layer
s = tf.placeholder('float', shape=[None, INPUT_SIZE, INPUT_SIZE, INPUT_CHANNEL], name='s')
# hidden conv layer
W_conv1 = weight_variable([8, 8, INPUT_CHANNEL, 32])
b_conv1 = bias_variable([32])
h_conv1 = tf.nn.relu(conv2d(s, W_conv1, 4) + b_conv1)
h_poo1 = max_pool_2x2(h_conv1)
W_conv2 = weight_variable([4, 4, 32, 64])
b_conv2 = bias_variable([64])
h_conv2 = tf.nn.relu(conv2d(h_poo1, W_conv2, 2) + b_conv2)
W_conv3 = weight_variable([3, 3, 64, 64])
b_conv3 = bias_variable([64])
h_conv3 = tf.nn.relu(conv2d(h_conv2, W_conv3, 1) + b_conv3)
h_conv3_out_size = np.prod(h_conv3.get_shape().as_list()[1:])
print h_conv3_out_size
h_conv3_flat = tf.reshape(h_conv3, [-1, h_conv3_out_size])
W_fc1 = weight_variable([h_conv3_out_size, 512])
b_fc1 = bias_variable([512])
h_fc1 = tf.nn.relu(tf.matmul(h_conv3_flat, W_fc1) + b_fc1)
# readout layer: Q_value
W_fc2 = weight_variable([512, ACTIONS_DIM])
b_fc2 = bias_variable([ACTIONS_DIM])
Q_value = tf.matmul(h_fc1, W_fc2) + b_fc2
self.s = s
self.Q_value = Q_value
return
def create_minimize(self):
self.a = tf.placeholder('float', shape=[None, ACTIONS_DIM])
self.y = tf.placeholder('float', shape=[None])
Q_action = tf.reduce_sum(tf.multiply(self.Q_value, self.a), reduction_indices=1)
self.loss = tf.reduce_mean(tf.square(self.y - Q_action))
self.optimizer = tf.train.AdamOptimizer(ALPHA)
self.apply_gradients = self.optimizer.minimize(self.loss)
return
def perceive(self, state, action, reward, next_state, terminal):
self.global_t += 1
self.replay_buffer.append((state, action, reward, next_state, terminal))
self.episode_reward += reward
if self.episode_start_time == 0.0:
self.episode_start_time = time.time()
if terminal or self.global_t % 600 == 0:
living_time = time.time() - self.episode_start_time
self.record_log(self.episode_reward, living_time)
if terminal:
self.episode_reward = 0.0
self.episode_start_time = time.time()
if len(self.replay_buffer) > BATCH_SIZE * 4:
self.train_Q_network()
return
def get_action_index(self, state):
Q_value_t = self.session.run(self.Q_value, feed_dict={self.s: [state]})[0]
return np.argmax(Q_value_t), np.max(Q_value_t)
def epsilon_greedy(self, state):
"""
:param state: 1x84x84x3
"""
Q_value_t = self.session.run(self.Q_value, feed_dict={self.s: [state]})[0]
action_index = 0
if random.random() <= self.epsilon:
action_index = random.randrange(ACTIONS_DIM)
else:
action_index = np.argmax(Q_value_t)
if self.epsilon > FINAL_EPSILON:
self.epsilon -= EPSILON_ANNEAL
max_q_value = np.max(Q_value_t)
return action_index, max_q_value
def train_Q_network(self):
'''
do backpropogation
'''
minibatch = random.sample(self.replay_buffer, BATCH_SIZE)
state_batch = [t[0] for t in minibatch]
action_batch = [t[1] for t in minibatch]
reward_batch = [t[2] for t in minibatch]
next_state_batch = [t[3] for t in minibatch]
terminal_batch = [t[4] for t in minibatch]
y_batch = []
Q_value_batch = self.session.run(self.Q_value, feed_dict={self.s: next_state_batch})
for i in range(BATCH_SIZE):
terminal = terminal_batch[i]
if terminal:
y_batch.append(reward_batch[i])
else:
y_batch.append(reward_batch[i] + GAMMA * np.max(Q_value_batch[i]))
self.session.run(self.apply_gradients, feed_dict={
self.y: y_batch,
self.a: action_batch,
self.s: state_batch
})
if self.global_t % 100000 == 0:
self.backup()
return
def record_log(self, reward, living_time):
'''
record the change of reward into tensorboard log
'''
summary_str = self.session.run(self.summary_op, feed_dict={
self.reward_input: reward,
self.time_input: living_time
})
self.summary_writer.add_summary(summary_str, self.global_t)
self.summary_writer.flush()
return
def restore(self):
checkpoint = tf.train.get_checkpoint_state(CHECKPOINT_DIR)
if checkpoint and checkpoint.model_checkpoint_path:
self.saver.restore(self.session, checkpoint.model_checkpoint_path)
print("checkpoint loaded:", checkpoint.model_checkpoint_path)
tokens = checkpoint.model_checkpoint_path.split("-")
# set global step
self.global_t = int(tokens[1])
print(">>> global step set: ", self.global_t)
else:
print("Could not find old checkpoint")
return
def backup(self):
if not os.path.exists(CHECKPOINT_DIR):
os.mkdir(CHECKPOINT_DIR)
self.saver.save(self.session, CHECKPOINT_DIR + '/' + 'checkpoint', global_step=self.global_t)
return
def main():
'''
the function for training
'''
agent = DQN()
game = FlappyBird()
game.reset()
s_t = game.s_t
while agent.global_t < MAX_TIME_STEP:
action_id, action_q = agent.epsilon_greedy(s_t)
game.process(action_id)
action = np.zeros(ACTIONS_DIM)
action[action_id] = 1
s_t1, reward, terminal = (game.s_t1, game.reward, game.terminal)
agent.perceive(s_t, action, reward, s_t1, terminal)
if agent.global_t % 10 == 0:
print 'global_t:', agent.global_t, '/ epsilon:', agent.epsilon, '/ terminal:', game.terminal, \
'/ action:', action_id, '/ reward:', game.reward, '/ q_value:', action_q
if game.terminal:
game.reset()
# s_t <- s_t1
s_t = s_t1
# game.update()
return
if __name__ == '__main__':
print 'dd'
main()