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simple_dqn.py
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simple_dqn.py
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import gym
import random
import numpy as np
import tensorflow as tf
class DQN:
REPLAY_MEMORY_SIZE = 10000
RANDOM_ACTION_PROB = 0.5
RANDOM_ACTION_DECAY = 0.99
HIDDEN1_SIZE = 128
HIDDEN2_SIZE = 128
NUM_EPISODES = 1000
MAX_STEPS = 1000
LEARNING_RATE = 0.0001
MINIBATCH_SIZE = 10
DISCOUNT_FACTOR = 0.9
TARGET_UPDATE_FREQ = 100
REG_FACTOR = 0.001
LOG_DIR = '/tmp/dqn'
def __init__(self, env):
self.env = gym.make(env)
assert len(self.env.observation_space.shape) == 1
self.input_size = self.env.observation_space.shape[0]
self.output_size = self.env.action_space.n
def init_network(self):
# Inference
self.x = tf.placeholder(tf.float32, [None, self.input_size])
with tf.name_scope('hidden1'):
W1 = tf.Variable(
tf.truncated_normal([self.input_size, self.HIDDEN1_SIZE],
stddev=0.01), name='W1')
b1 = tf.Variable(tf.zeros(self.HIDDEN1_SIZE), name='b1')
h1 = tf.nn.tanh(tf.matmul(self.x, W1) + b1)
with tf.name_scope('hidden2'):
W2 = tf.Variable(
tf.truncated_normal([self.HIDDEN1_SIZE, self.HIDDEN2_SIZE],
stddev=0.01), name='W2')
b2 = tf.Variable(tf.zeros(self.HIDDEN2_SIZE), name='b2')
h2 = tf.nn.tanh(tf.matmul(h1, W2) + b2)
with tf.name_scope('output'):
W3 = tf.Variable(
tf.truncated_normal([self.HIDDEN2_SIZE, self.output_size],
stddev=0.01), name='W3')
b3 = tf.Variable(tf.zeros(self.output_size), name='b3')
self.Q = tf.matmul(h2, W3) + b3
self.weights = [W1, b1, W2, b2, W3, b3]
# Loss
self.targetQ = tf.placeholder(tf.float32, [None])
self.targetActionMask = tf.placeholder(
tf.float32, [None, self.output_size])
# TODO: Optimize this
q_values = tf.reduce_sum(tf.multiply(self.Q, self.targetActionMask),
reduction_indices=[1])
self.loss = tf.reduce_mean(tf.square(tf.subtract(q_values, self.targetQ)))
# Reguralization
for w in [W1, W2, W3]:
self.loss += self.REG_FACTOR * tf.reduce_sum(tf.square(w))
# Training
optimizer = tf.train.GradientDescentOptimizer(self.LEARNING_RATE)
global_step = tf.Variable(0, name='global_step', trainable=False)
self.train_op = optimizer.minimize(self.loss, global_step=global_step)
def train(self, num_episodes=NUM_EPISODES):
replay_memory = []
self.session = tf.Session()
# Summary for TensorBoard
#tf.scalar_summary('loss', self.loss)
#self.summary = tf.merge_all_summaries()
#self.summary_writer = tf.train.SummaryWriter(
# self.LOG_DIR, self.session.graph)
self.session.run(tf.initialize_all_variables())
total_steps = 0
for episode in range(num_episodes):
print("Training: Episode = %d, Global step = %d" %
(episode, total_steps))
state = self.env.reset()
target_weights = self.session.run(self.weights)
for step in range(self.MAX_STEPS):
# Pick the next action and execute it
action = None
if random.random() < self.RANDOM_ACTION_PROB:
action = self.env.action_space.sample()
else:
q_values = self.session.run(
self.Q, feed_dict={self.x: [state]})
action = q_values.argmax()
self.RANDOM_ACTION_PROB *= self.RANDOM_ACTION_DECAY
obs, reward, done, _ = self.env.step(action)
# Update replay memory
if done:
reward = -100
replay_memory.append((state, action, reward, obs, done))
if len(replay_memory) > self.REPLAY_MEMORY_SIZE:
replay_memory.pop(0)
state = obs
# Sample a random minibatch and fetch max Q at s'
if len(replay_memory) >= self.MINIBATCH_SIZE:
minibatch = random.sample(
replay_memory, self.MINIBATCH_SIZE)
next_states = [m[3] for m in minibatch]
# TODO: Optimize to skip terminal states
feed_dict = {self.x: next_states}
feed_dict.update(zip(self.weights, target_weights))
q_values = self.session.run(self.Q, feed_dict=feed_dict)
max_q_values = q_values.max(axis=1)
# Compute target Q values
target_q = np.zeros(self.MINIBATCH_SIZE)
target_action_mask = np.zeros(
(self.MINIBATCH_SIZE, self.output_size), dtype=int)
for i in range(self.MINIBATCH_SIZE):
_, action, reward, _, terminal = minibatch[i]
target_q[i] = reward
if not terminal:
target_q[i] += self.DISCOUNT_FACTOR * \
max_q_values[i]
target_action_mask[i][action] = 1
# Gradient descent
states = [m[0] for m in minibatch]
feed_dict = {
self.x: states,
self.targetQ: target_q,
self.targetActionMask: target_action_mask,
}
_ = self.session.run([self.train_op],
feed_dict=feed_dict)
# Update target weights
if total_steps % self.TARGET_UPDATE_FREQ == 0:
target_weights = self.session.run(self.weights)
total_steps += 1
if done:
break
def play(self):
state = self.env.reset()
done = False
steps = 0
while not done and steps < 200:
self.env.render()
q_values = self.session.run(self.Q, feed_dict={self.x: [state]})
action = q_values.argmax()
state, _, done, _ = self.env.step(action)
steps += 1
return steps
if __name__ == '__main__':
dqn = DQN('CartPole-v0')
dqn.init_network()
#dqn.env.monitor.start('/tmp/cartpole')
dqn.train()
#dqn.env.monitor.close()
res = []
for i in range(100):
steps = dqn.play()
print("Test steps = ", steps)
res.append(steps)
print("Mean steps = ", sum(res) / len(res))