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DRQN.py
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DRQN.py
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import tensorflow as tf
import numpy as np
import random
import time
import os
import sys
from netutil import *
from game.flappy_bird import FlappyBird
from replay_buffer import ReplayBuffer
INPUT_SIZE = 84
INPUT_CHANNEL = 4
ACTIONS_DIM = 2
LSTM_UNITS = 256
LSTM_MAX_STEP = 8
GAMMA = 0.99
FINAL_EPSILON = 0.0001
INITIAL_EPSILON = 0.0001
ALPHA = 1e-6 # the learning rate of optimizer
TAU = 0.001
UPDATE_FREQUENCY = 5 # the frequency to update target network
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
BATCH_SIZE = 4
REPLAY_MEMORY = 2000
CHECKPOINT_DIR = 'tmp_drqn/checkpoints'
LOG_FILE = 'tmp_drqn/log'
class Network(object):
def __init__(self, scope_name):
with tf.variable_scope(scope_name) as scope:
# input layer
self.state_input = tf.placeholder('float', shape=[None, INPUT_SIZE, INPUT_SIZE, INPUT_CHANNEL])
# hidden conv layer
self.W_conv1 = weight_variable([8, 8, INPUT_CHANNEL, 32])
self.b_conv1 = bias_variable([32])
h_conv1 = tf.nn.relu(conv2d(self.state_input, self.W_conv1, 4) + self.b_conv1)
h_poo1 = max_pool_2x2(h_conv1)
self.W_conv2 = weight_variable([4, 4, 32, 64])
self.b_conv2 = bias_variable([64])
h_conv2 = tf.nn.relu(conv2d(h_poo1, self.W_conv2, 2) + self.b_conv2)
self.W_conv3 = weight_variable([3, 3, 64, 64])
self.b_conv3 = bias_variable([64])
h_conv3 = tf.nn.relu(conv2d(h_conv2, self.W_conv3, 1) + self.b_conv3)
h_conv3_out_size = np.prod(h_conv3.get_shape().as_list()[1:])
h_conv3_flat = tf.reshape(h_conv3, [-1, h_conv3_out_size])
self.W_fc1 = weight_variable([h_conv3_out_size, LSTM_UNITS])
self.b_fc1 = bias_variable([LSTM_UNITS])
h_fc1 = tf.nn.relu(tf.matmul(h_conv3_flat, self.W_fc1) + self.b_fc1)
# reshape to fit lstm (batch_size, timestep, LSTM_UNITS)
self.timestep = tf.placeholder(dtype=tf.int32)
self.batch_size = tf.placeholder(dtype=tf.int32)
h_fc1_reshaped = tf.reshape(h_fc1, [self.batch_size, self.timestep, LSTM_UNITS])
self.lstm_cell = tf.contrib.rnn.BasicLSTMCell(num_units=LSTM_UNITS, state_is_tuple=True)
self.initial_lstm_state = self.lstm_cell.zero_state(self.batch_size, tf.float32)
lstm_outputs, self.lstm_state = tf.nn.dynamic_rnn(
self.lstm_cell,
h_fc1_reshaped,
initial_state=self.initial_lstm_state,
sequence_length=self.timestep,
time_major=False,
dtype=tf.float32,
scope=scope
)
print 'lstm shape:', lstm_outputs.get_shape()
# shape: [batch_size*timestep, LSTM_UNITS]
lstm_outputs = tf.reshape(lstm_outputs, [-1, LSTM_UNITS])
# option1: for separate channel
# streamA, streamV = tf.split(lstm_outputs, 2, axis=1)
# self.AW = tf.Variable(tf.random_normal([LSTM_UNITS / 2, ACTIONS_DIM]))
# self.VW = tf.Variable(tf.random_normal([LSTM_UNITS / 2, 1]))
# advantage = tf.matmul(streamA, self.AW)
# value = tf.matmul(streamV, self.VW)
# self.Q_value = value + tf.subtract(advantage, tf.reduce_mean(advantage, axis=1, keep_dims=True))
# option2: for fully-connected
self.W_fc2 = weight_variable([LSTM_UNITS, ACTIONS_DIM])
self.b_fc2 = bias_variable([ACTIONS_DIM])
self.Q_value = tf.matmul(lstm_outputs, self.W_fc2) + self.b_fc2
self.Q_action = tf.argmax(self.Q_value, 1)
print 'Q shape:', self.Q_value.get_shape()
scope.reuse_variables()
self.W_lstm = tf.get_variable("basic_lstm_cell/weights")
self.b_lstm = tf.get_variable("basic_lstm_cell/biases")
return
def get_vars(self):
return [
self.W_conv1, self.b_conv1,
self.W_conv2, self.b_conv2,
self.W_conv3, self.b_conv3,
self.W_fc1, self.b_fc1,
self.W_lstm, self.b_lstm,
# self.AW, self.VW
self.W_fc2, self.b_fc2,
]
class DRQN(object):
def __init__(self):
self.global_t = 0
self.replay_buffer = ReplayBuffer(REPLAY_MEMORY)
# q-network parameter
self.create_network()
self.create_minimize()
# init session
self.session = tf.InteractiveSession()
self.session.run(tf.global_variables_initializer())
# update_target(self.session, self.target_ops)
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):
self.main_net = Network(scope_name='main')
# self.target_net = Network(scope_name='target')
# self.target_ops = update_target_graph_op(tf.trainable_variables(), TAU)
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.main_net.Q_value, self.a), axis=1)
self.full_loss = tf.reduce_mean(tf.square(self.y - Q_action))
# maskA = tf.zeros([BATCH_SIZE, LSTM_MAX_STEP // 2])
# maskB = tf.ones([BATCH_SIZE, LSTM_MAX_STEP // 2])
# mask = tf.concat([maskA, maskB], axis=1)
# mask = tf.reshape(mask, [-1])
# just use a half loss with the mask:[0 0 0 0 1 1 1 1]
# self.loss = tf.multiply(self.full_loss, mask)
self.optimizer = tf.train.AdamOptimizer(learning_rate=ALPHA)
self.apply_gradients = self.optimizer.minimize(self.full_loss)
# self.optimizer = tf.train.RMSPropOptimizer(learning_rate=ALPHA, decay=0.99)
# self.gradients = tf.gradients(self.loss, self.main_net.get_vars())
# clip_grads = [tf.clip_by_norm(grad, 40.0) for grad in self.gradients]
# self.apply_gradients = self.optimizer.apply_gradients(zip(clip_grads, self.main_net.get_vars()))
return
def perceive(self, state, action, reward, next_state, terminal):
self.global_t += 1
self.episode_reward += reward
if self.episode_start_time == 0.0:
self.episode_start_time = time.time()
if terminal or self.global_t % 20 == 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 self.replay_buffer.size() > BATCH_SIZE:
self.train_Q_network()
if self.global_t % 100000 == 0:
self.backup()
return
def epsilon_greedy(self, state, lstm_state_in):
"""
:param state: 1x84x84x3
"""
Q_value_t, lstm_state_out = self.session.run(
[self.main_net.Q_value, self.main_net.lstm_state],
feed_dict={
self.main_net.state_input: [state],
self.main_net.initial_lstm_state: lstm_state_in,
self.main_net.batch_size: 1,
self.main_net.timestep: 1
})
Q_value_t = Q_value_t[0]
action_index = 0
if random.random() <= self.epsilon:
action_index = random.randrange(ACTIONS_DIM)
print 'random-index:', action_index
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, lstm_state_out
def train_Q_network(self):
'''
do backpropogation
'''
# len(minibatch) = BATCH_SIZE * LSTM_MAX_STEP
# if self.global_t % (UPDATE_FREQUENCY * 1000) == 0:
# update_target(self.session, self.target_ops)
# limit the training frequency
# if self.global_t % UPDATE_FREQUENCY != 0:
# return
minibatch = self.replay_buffer.sample(BATCH_SIZE, LSTM_MAX_STEP)
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 = []
# todo: need to feed with batch_size, timestep, lstm_state
lstm_state_train = (np.zeros([BATCH_SIZE, LSTM_UNITS]), np.zeros([BATCH_SIZE, LSTM_UNITS]))
Q_target = self.session.run(
self.main_net.Q_value,
feed_dict={
self.main_net.state_input: next_state_batch,
self.main_net.initial_lstm_state: lstm_state_train,
self.main_net.batch_size: BATCH_SIZE,
self.main_net.timestep: LSTM_MAX_STEP
}
)
# Q_action = self.session.run(
# self.target_net.Q_action,
# feed_dict={
# self.target_net.state_input: next_state_batch,
# self.target_net.initial_lstm_state: lstm_state_train,
# self.target_net.batch_size: BATCH_SIZE,
# self.target_net.timestep: LSTM_MAX_STEP
# }
# )
for i in range(len(state_batch)):
terminal = terminal_batch[i]
if terminal:
y_batch.append(reward_batch[i])
else:
y_batch.append(reward_batch[i] + GAMMA * np.max(Q_target[i]))
# y_batch.append(reward_batch[i] + GAMMA * Q_value[i][Q_action[i]])
self.session.run(self.apply_gradients, feed_dict={
self.y: y_batch,
self.a: action_batch,
self.main_net.state_input: state_batch,
self.main_net.initial_lstm_state: lstm_state_train,
self.main_net.batch_size: BATCH_SIZE,
self.main_net.timestep: LSTM_MAX_STEP
})
# print loss
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 = DRQN()
env = FlappyBird()
while True:
env.reset()
episode_buffer = []
lstm_state = (np.zeros([1, LSTM_UNITS]), np.zeros([1, LSTM_UNITS]))
s_t = env.s_t
while not env.terminal:
# action_id = random.randint(0, 1)
action_id, action_q, lstm_state = agent.epsilon_greedy(s_t, lstm_state)
env.process(action_id)
action = np.zeros(ACTIONS_DIM)
action[action_id] = 1
s_t1, reward, terminal = (env.s_t1, env.reward, env.terminal)
# frame skip
episode_buffer.append((s_t, action, reward, s_t1, 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:', terminal, \
'/ action:', action_id, '/ reward:', reward, '/ q_value:', action_q
# s_t <- s_t1
s_t = s_t1
if len(episode_buffer) >= 50:
# start a new episode buffer, in case of an over-long memory
agent.replay_buffer.add(episode_buffer)
episode_buffer = []
print '----------- episode buffer > 100---------'
# reset the state
if len(episode_buffer) > LSTM_MAX_STEP:
agent.replay_buffer.add(episode_buffer)
print 'episode_buffer', len(episode_buffer)
print 'replay_buffer.size:', agent.replay_buffer.size()
# break
return
if __name__ == '__main__':
main()