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20171220-dueling-dqn.py
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20171220-dueling-dqn.py
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# ==============================================================================
# Dueling DQN, compared with DQN, on OpenAI gym CartPole.
# ------------------------------------------------------------------------------
# Code adapted from: https://github.com/MorvanZhou/Reinforcement-learning-with-
# tensorflow/tree/master/contents/5.3_Dueling_DQN
#
# Practice Deep Reinforcement Learning with Tensorflow
#
# Deep Q Network is a way to do Reinforcement Learning. Specifically, it uses a
# neural network to approximate the expected reward of taking action a at
# state s, denoted as Q(s,a). Then, when playing a game, the player could
# choose the action that maximize the expected reward for the current state.
# Q is usually calculated by:
# Q(s, a) = reward + reward_decay * max[Q(s_, a_)]
# where max[Q(s_, a_)] is the best reward for the next state s_
#
# Double DQN tries to avoid overestimating max[Q(s_, a_)]. This might happen to
# DQN because it uses the same Q value to select and evaluate an action.
# In double DQN, we copy the Q value to Q_next and calculate
# max[Q_next(s_, a_)]. While updating Q values, we freeze the Q_next.
# We only copy Q value to Q_next at a given frequency.
#
# Dueling Q Network considers Q(s, a) = V(s) + A(s,a) where
# i) V is the "value" of a state and is independent from whatever action
# ii) A(s,a) is the "advantage" of taking action a at state s.
# The advantage is calculated relative to all possible actions at s.
#
# This code compares performance of DQN and DuelingQN, while both uses double
# Q network, on a OpenAI Gym Pendulum game.
#
# ==============================================================================
#
import gym
import matplotlib.pyplot as plt
import numpy as np
import tensorflow as tf
np.random.seed(13)
tf.set_random_seed(13)
env = gym.make('Pendulum-v0')
env = env.unwrapped
env.seed(7)
ACTION_SPACE = 25 # Discretize the continuous action space of cartpole
MEMORY_SIZE = 3000
train_iter = 20000
train_freq = 10
def main():
sess = tf.Session()
with tf.variable_scope('natural'):
natural_DQN = DuelingDQN(
n_actions=ACTION_SPACE, n_features=3, memory_size=MEMORY_SIZE,
e_greedy_increment=None, sess=sess, dueling=False)
with tf.variable_scope('dueling'):
dueling_DQN = DuelingDQN(
n_actions=ACTION_SPACE, n_features=3, memory_size=MEMORY_SIZE,
e_greedy_increment=None, sess=sess, dueling=True)
sess.run(tf.global_variables_initializer())
l_natural, r_natural = train(natural_DQN)
l_dueling, r_dueling = train(dueling_DQN)
plt.figure(1)
plt.plot(np.array(l_natural), c='r', label='natural')
plt.plot(np.array(l_dueling), c='b', label='dueling')
plt.legend(loc='best')
plt.ylabel('Loss')
plt.xlabel('training steps')
plt.grid()
plt.figure(2)
plt.plot(np.array(r_natural), c='r', label='natural')
plt.plot(np.array(r_dueling), c='b', label='dueling')
plt.legend(loc='best')
plt.ylabel('accumulated reward')
plt.xlabel('training steps')
plt.grid()
plt.show()
def train(RL):
acc_r = [0]
observation = env.reset()
for step in range(train_iter):
action = RL.choose_action(observation)
f_action = (action-(ACTION_SPACE-1)/2)/((ACTION_SPACE-1)/4) # [-2 ~ 2] float actions
observation_, reward, _, _= env.step(np.array([f_action]))
acc_r.append(reward + acc_r[-1])
RL.store_transition(observation, action, reward, observation_)
observation = observation_
if ( step % train_freq == 0 and
step > RL.batch_size): RL.learn()
#if step % 100 == 0: env.render()
return RL.loss_his, acc_r
class DuelingDQN:
def __init__(
self,
n_actions,
n_features,
learning_rate=1e-3,
reward_decay=0.9,
e_greedy=0.9,
replace_target_iter=200, # Update Target Network Value
memory_size=500,
batch_size=100,
e_greedy_increment=None,
dueling=True,
sess=None):
self.n_actions = n_actions
self.n_features = n_features
self.learning_rate = learning_rate
self.reward_decay = reward_decay
self.memory_size = memory_size
self.batch_size = batch_size
self.dueling = dueling
self.replace_target_iter = replace_target_iter
self.epsilon_max = e_greedy
self.epsilon_increment = e_greedy_increment
self.epsilon = e_greedy if e_greedy_increment is None else 0
self.learn_step_counter = 0
self.memory = np.zeros((self.memory_size, n_features*2+2)) #For [s,[a,r],s_]
self.memory_counter = 0
self.loss_his = []
self.sess = sess
self._build_net()
def _build_net(self):
def build_layers(s, c_names, n_l1, w_init, b_init):
with tf.variable_scope('l1'):
w1 = tf.get_variable('w1', [self.n_features, n_l1], initializer=w_initializer, collections=c_names)
b1 = tf.get_variable('b1', [1, n_l1], initializer=b_initializer, collections=c_names)
l1 = tf.nn.relu(tf.matmul(s, w1) + b1)
if self.dueling:
# Dueling DQN
with tf.variable_scope('Value'):
w2 = tf.get_variable('w2', [n_l1, 1], initializer=w_initializer, collections=c_names)
b2 = tf.get_variable('b2', [1, 1], initializer=b_initializer, collections=c_names)
self.V = tf.matmul(l1, w2) + b2
with tf.variable_scope('Advantage'):
w2 = tf.get_variable('w2', [n_l1, self.n_actions], initializer=w_initializer, collections=c_names)
b2 = tf.get_variable('b2', [1, self.n_actions], initializer=b_initializer, collections=c_names)
self.A = tf.matmul(l1, w2) + b2
with tf.variable_scope('Q'):
# Q = V(s) + A(s,a)
out = self.V + (self.A - tf.reduce_mean(self.A, axis=1, keep_dims=True))
else:
with tf.variable_scope('Q'):
w2 = tf.get_variable('w2', [n_l1, self.n_actions], initializer=w_initializer, collections=c_names)
b2 = tf.get_variable('b2', [1, self.n_actions], initializer=b_initializer, collections=c_names)
out = tf.matmul(l1, w2) + b2
return out
# State
self.s = tf.placeholder(tf.float32, [None, self.n_features], name='s')
# Placeholder for the desired Q value
self.q_target = tf.placeholder(tf.float32, [None, self.n_actions], name='Q_target')
# Evaluate expected reward for the current state
with tf.variable_scope('eval_net'):
c_names, n_l1, w_initializer, b_initializer = \
['eval_net_params', tf.GraphKeys.GLOBAL_VARIABLES], 20, \
tf.random_normal_initializer(0., 0.3), tf.constant_initializer(0.1) # config of layers
self.q_eval = build_layers(self.s, c_names, n_l1, w_initializer, b_initializer)
# Define loss and training operation
with tf.variable_scope('loss'):
self.loss = tf.reduce_mean(tf.squared_difference(self.q_target, self.q_eval))
with tf.variable_scope('train'):
self._train_op = tf.train.RMSPropOptimizer(self.learning_rate).minimize(self.loss)
# Every training step we are changing q_eval, thus q_eval can be unstable
# Instead of letting the next state chooses the action that maximize q_eval,
# Copy q_eval to q_next at a certain frequency, and let the next state
# choose the action that maximize q_next
self.s_ = tf.placeholder(tf.float32, [None, self.n_features], name='s_')
with tf.variable_scope('copy_net'):
c_names = ['copy_net_params', tf.GraphKeys.GLOBAL_VARIABLES]
self.q_next = build_layers(self.s_, c_names, n_l1, w_initializer, b_initializer)
# Update q_eval and q_next at a frequency
t_params = tf.get_collection('copy_net_params')
e_params = tf.get_collection('eval_net_params')
self.replace_target_op = [tf.assign(t, e) for t, e in zip(t_params, e_params)]
def store_transition(self, s, a, r, s_):
transition = np.hstack((s, [a, r], s_))
index = self.memory_counter % self.memory_size
self.memory[index, :] = transition
self.memory_counter += 1
def unpack_transition(self, index):
batch_memory = self.memory[index, :]
s = batch_memory[:, :self.n_features]
s_ = batch_memory[:, -self.n_features:]
a = batch_memory[:, self.n_features].astype(int)
r = batch_memory[:, self.n_features + 1]
return (s, a, r, s_)
def choose_action(self, observation):
observation = observation[np.newaxis, :]
if np.random.uniform() < self.epsilon: # choosing action
actions_value = self.sess.run(self.q_eval, feed_dict={self.s: observation})
action = np.argmax(actions_value)
else:
action = np.random.randint(0, self.n_actions)
return action
def learn(self):
if self.learn_step_counter % self.replace_target_iter == 0:
self.sess.run(self.replace_target_op)
print('q_next_replaced')
# Select a batch
sample_index = np.random.choice(self.memory_size, size=self.batch_size)
s, a, r, s_ = self.unpack_transition(sample_index)
batch_index = np.arange(self.batch_size, dtype=np.int32)
q_next = self.sess.run(self.q_next, feed_dict={self.s_: s_})
q_eval = self.sess.run(self.q_eval, {self.s: s})
q_target = q_eval.copy()
q_target[batch_index, a] = r + self.reward_decay * np.max(q_next, axis=1)
_, loss = self.sess.run([self._train_op, self.loss],
feed_dict={self.s: s, self.q_target: q_target})
self.loss_his.append(loss)
self.epsilon = self.epsilon + self.epsilon_increment if self.epsilon < self.epsilon_max else self.epsilon_max
self.learn_step_counter += 1
if __name__ == '__main__': main()