Deep Q Learning
使用gym的CartPole作为环境，使用QDN解决离散动作空间的问题。
一、导入需要的包和定义超参数
import tensorflow as tf
import numpy as np
import gym
import time
import random
from collections import deque ##################### hyper parameters #################### # Hyper Parameters for DQN
GAMMA = 0.9 # discount factor for target Q
INITIAL_EPSILON = 0.5 # starting value of epsilon
FINAL_EPSILON = 0.01 # final value of epsilon
REPLAY_SIZE = 10000 # experience replay buffer size
BATCH_SIZE = 32 # size of minibatch
二、DQN构造函数
1、初始化经验重放buffer；
2、设置问题的状态空间维度，动作空间维度；
3、设置e-greedy的epsilon；
4、创建用于估计q值的Q网络，创建训练方法。
5、初始化tensorflow的session
def __init__(self, env):
# init experience replay
self.replay_buffer = deque()
# init some parameters
self.time_step = 0
self.epsilon = INITIAL_EPSILON
self.state_dim = env.observation_space.shape[0]
self.action_dim = env.action_space.n self.create_Q_network()
self.create_training_method() # Init session
self.session = tf.InteractiveSession()
self.session.run(tf.global_variables_initializer())
三、创建神经网络
创建一个3层全连接的神经网络，hidden layer有20个神经元。
def create_Q_network(self):
# network weights
W1 = self.weight_variable([self.state_dim,20])
b1 = self.bias_variable([20])
W2 = self.weight_variable([20,self.action_dim])
b2 = self.bias_variable([self.action_dim])
# input layer
self.state_input = tf.placeholder("float",[None,self.state_dim])
# hidden layers
h_layer = tf.nn.relu(tf.matmul(self.state_input,W1) + b1)
# Q Value layer
self.Q_value = tf.matmul(h_layer,W2) + b2 def weight_variable(self,shape):
initial = tf.truncated_normal(shape)
return tf.Variable(initial) def bias_variable(self,shape):
initial = tf.constant(0.01, shape = shape)
return tf.Variable(initial)
定义cost function和优化的方法，使“实际”q值(y)与当前网络估计的q值的差值尽可能小，即使当前网络尽可能接近真实的q值。
def create_training_method(self):
self.action_input = tf.placeholder("float",[None,self.action_dim]) # one hot presentation
self.y_input = tf.placeholder("float",[None])
Q_action = tf.reduce_sum(tf.multiply(self.Q_value,self.action_input),reduction_indices = 1)
self.cost = tf.reduce_mean(tf.square(self.y_input - Q_action))
self.optimizer = tf.train.AdamOptimizer(0.0001).minimize(self.cost)
从buffer中随机取样BATCH_SIZE大小的样本，计算y(batch中(s,a)在当前网络下的实际q值)
if done: y_batch.append(reward_batch[i])
else : y_batch.append(reward_batch[i] + GAMMA * np.max(Q_value_batch[i]))
def train_Q_network(self):
self.time_step += 1
# Step 1: obtain random minibatch from replay memory
minibatch = random.sample(self.replay_buffer,BATCH_SIZE)
state_batch = [data[0] for data in minibatch]
action_batch = [data[1] for data in minibatch]
reward_batch = [data[2] for data in minibatch]
next_state_batch = [data[3] for data in minibatch] # Step 2: calculate y
y_batch = []
Q_value_batch = self.Q_value.eval(feed_dict={self.state_input:next_state_batch})
for i in range(0,BATCH_SIZE):
done = minibatch[i][4]
if done:
y_batch.append(reward_batch[i])
else :
y_batch.append(reward_batch[i] + GAMMA * np.max(Q_value_batch[i])) self.optimizer.run(feed_dict={
self.y_input:y_batch,
self.action_input:action_batch,
self.state_input:state_batch
})
四、Agent感知环境的接口
每次决策采取的动作，得到环境的反馈，将(s, a, r, s_, done)存入经验重放buffer。当buffer中经验数量大于batch_size时开始训练。
def perceive(self,state,action,reward,next_state,done):
one_hot_action = np.zeros(self.action_dim)
one_hot_action[action] = 1
self.replay_buffer.append((state,one_hot_action,reward,next_state,done))
if len(self.replay_buffer) > REPLAY_SIZE:
self.replay_buffer.popleft() if len(self.replay_buffer) > BATCH_SIZE:
self.train_Q_network()
五、决策(选取action)
两种选取方式greedy和e-greedy。
def egreedy_action(self,state):
Q_value = self.Q_value.eval(feed_dict = {
self.state_input:[state]
})[0]
if random.random() <= self.epsilon:
self.epsilon -= (INITIAL_EPSILON - FINAL_EPSILON) / 10000
return random.randint(0,self.action_dim - 1)
else:
self.epsilon -= (INITIAL_EPSILON - FINAL_EPSILON) / 10000
return np.argmax(Q_value) def action(self,state):
return np.argmax(self.Q_value.eval(feed_dict = {
self.state_input:[state]
})[0])
Agent完整代码：
DQN
import tensorflow as tf
import numpy as np
import gym
import time
import random
from collections import deque ##################### hyper parameters #################### # Hyper Parameters for DQN
GAMMA = 0.9 # discount factor for target Q
INITIAL_EPSILON = 0.5 # starting value of epsilon
FINAL_EPSILON = 0.01 # final value of epsilon
REPLAY_SIZE = 10000 # experience replay buffer size
BATCH_SIZE = 32 # size of minibatch ############################### DQN #################################### class DQN():
# DQN Agent
def __init__(self, env):
# init experience replay
self.replay_buffer = deque()
# init some parameters
self.time_step = 0
self.epsilon = INITIAL_EPSILON
self.state_dim = env.observation_space.shape[0]
self.action_dim = env.action_space.n self.create_Q_network()
self.create_training_method() # Init session
self.session = tf.InteractiveSession()
self.session.run(tf.global_variables_initializer()) def create_Q_network(self):
# network weights
W1 = self.weight_variable([self.state_dim,20])
b1 = self.bias_variable([20])
W2 = self.weight_variable([20,self.action_dim])
b2 = self.bias_variable([self.action_dim])
# input layer
self.state_input = tf.placeholder("float",[None,self.state_dim])
# hidden layers
h_layer = tf.nn.relu(tf.matmul(self.state_input,W1) + b1)
# Q Value layer
self.Q_value = tf.matmul(h_layer,W2) + b2 def create_training_method(self):
self.action_input = tf.placeholder("float",[None,self.action_dim]) # one hot presentation
self.y_input = tf.placeholder("float",[None])
Q_action = tf.reduce_sum(tf.multiply(self.Q_value,self.action_input),reduction_indices = 1)
self.cost = tf.reduce_mean(tf.square(self.y_input - Q_action))
self.optimizer = tf.train.AdamOptimizer(0.0001).minimize(self.cost) def perceive(self,state,action,reward,next_state,done):
one_hot_action = np.zeros(self.action_dim)
one_hot_action[action] = 1
self.replay_buffer.append((state,one_hot_action,reward,next_state,done))
if len(self.replay_buffer) > REPLAY_SIZE:
self.replay_buffer.popleft() if len(self.replay_buffer) > BATCH_SIZE:
self.train_Q_network() def train_Q_network(self):
self.time_step += 1
# Step 1: obtain random minibatch from replay memory
minibatch = random.sample(self.replay_buffer,BATCH_SIZE)
state_batch = [data[0] for data in minibatch]
action_batch = [data[1] for data in minibatch]
reward_batch = [data[2] for data in minibatch]
next_state_batch = [data[3] for data in minibatch] # Step 2: calculate y
y_batch = []
Q_value_batch = self.Q_value.eval(feed_dict={self.state_input:next_state_batch})
for i in range(0,BATCH_SIZE):
done = minibatch[i][4]
if done:
y_batch.append(reward_batch[i])
else :
y_batch.append(reward_batch[i] + GAMMA * np.max(Q_value_batch[i])) self.optimizer.run(feed_dict={
self.y_input:y_batch,
self.action_input:action_batch,
self.state_input:state_batch
}) def egreedy_action(self,state):
Q_value = self.Q_value.eval(feed_dict = {
self.state_input:[state]
})[0]
if random.random() <= self.epsilon:
self.epsilon -= (INITIAL_EPSILON - FINAL_EPSILON) / 10000
return random.randint(0,self.action_dim - 1)
else:
self.epsilon -= (INITIAL_EPSILON - FINAL_EPSILON) / 10000
return np.argmax(Q_value) def action(self,state):
return np.argmax(self.Q_value.eval(feed_dict = {
self.state_input:[state]
})[0]) def weight_variable(self,shape):
initial = tf.truncated_normal(shape)
return tf.Variable(initial) def bias_variable(self,shape):
initial = tf.constant(0.01, shape = shape)
return tf.Variable(initial)
训练agent：
train.py
from DQN import DQN
import gym
import numpy as np
import time ENV_NAME = 'CartPole-v1'
EPISODE = 3000 # Episode limitation
STEP = 300 # Step limitation in an episode
TEST = 10 # The number of experiment test every 100 episode def main():
# initialize OpenAI Gym env and dqn agent
env = gym.make(ENV_NAME)
agent = DQN(env) for episode in range(EPISODE):
# initialize task
state = env.reset()
# Train
ep_reward = 0
for step in range(STEP):
action = agent.egreedy_action(state) # e-greedy action for train
next_state,reward,done,_ = env.step(action)
# Define reward for agent
reward = -10 if done else 1
ep_reward += reward
agent.perceive(state,action,reward,next_state,done)
state = next_state
if done:
#print('episode complete, reward: ', ep_reward)
break
# Test every 100 episodes
if episode % 100 == 0:
total_reward = 0
for i in range(TEST):
state = env.reset()
for j in range(STEP):
#env.render()
action = agent.action(state) # direct action for test
state,reward,done,_ = env.step(action)
total_reward += reward
if done:
break
ave_reward = total_reward/TEST
print ('episode: ',episode,'Evaluation Average Reward:',ave_reward) if __name__ == '__main__':
main()
reference:
https://www.cnblogs.com/pinard/p/9714655.html
https://github.com/ljpzzz/machinelearning/blob/master/reinforcement-learning/dqn.py