DQN算法对Atari训练代码

DQN算法对Atari训练的代码如下：

import gym
import numpy as np
import tensorflow as tf

env = gym.make('SpaceInvaders-v0')
state_size = env.observation_space.shape
action_size = env.action_space.n

# Hyperparameters
learning_rate = 0.001
memory_size = 1000000
batch_size = 32
gamma = 0.99
epsilon = 1.0
epsilon_min = 0.01
epsilon_decay = 0.995
target_update_frequency = 10000
num_episodes = 10000
max_steps = 5000

# Replay Memory
memory = []

# Q-Network
class QNetwork:
    def __init__(self, state_size, action_size, learning_rate):
        self.state_size = state_size
        self.action_size = action_size
        self.learning_rate = learning_rate
        
        self.inputs = tf.placeholder(tf.float32, [None, *state_size])
        self.actions = tf.placeholder(tf.float32, [None, action_size])
        self.targets = tf.placeholder(tf.float32, [None])
        
        conv1 = tf.layers.conv2d(inputs=self.inputs, filters=32, kernel_size=[8,8], strides=[4,4], padding="VALID", activation=tf.nn.relu)
        conv2 = tf.layers.conv2d(inputs=conv1, filters=64, kernel_size=[4,4], strides=[2,2], padding="VALID", activation=tf.nn.relu)
        conv3 = tf.layers.conv2d(inputs=conv2, filters=64, kernel_size=[3,3], strides=[1,1], padding="VALID", activation=tf.nn.relu)
        flatten = tf.layers.flatten(conv3)
        fc1 = tf.layers.dense(inputs=flatten, units=512, activation=tf.nn.relu)
        self.output = tf.layers.dense(inputs=fc1, units=action_size)
        
        self.loss = tf.reduce_mean(tf.square(self.targets - tf.reduce_sum(tf.multiply(self.output, self.actions), axis=1)))
        self.optimizer = tf.train.AdamOptimizer(learning_rate=self.learning_rate).minimize(self.loss)

# DQN Agent
class DQNAgent:
    def __init__(self, state_size, action_size, learning_rate, memory_size, batch_size, gamma, epsilon, epsilon_min, epsilon_decay, target_update_frequency):
        self.state_size = state_size
        self.action_size = action_size
        self.learning_rate = learning_rate
        self.memory_size = memory_size
        self.batch_size = batch_size
        self.gamma = gamma
        self.epsilon = epsilon
        self.epsilon_min = epsilon_min
        self.epsilon_decay = epsilon_decay
        self.target_update_frequency = target_update_frequency
        
        self.q_network = QNetwork(state_size, action_size, learning_rate)
        self.target_network = QNetwork(state_size, action_size, learning_rate)
        self.replay_memory = []
        self.timestep = 0
        
        self.sess = tf.Session()
        self.sess.run(tf.global_variables_initializer())
    
    def act(self, state):
        if np.random.rand() <= self.epsilon:
            return np.random.choice(self.action_size)
        q_values = self.sess.run(self.q_network.output, feed_dict={self.q_network.inputs: state.reshape(1, *self.state_size)})
        return np.argmax(q_values[0])
    
    def remember(self, state, action, reward, next_state, done):
        self.replay_memory.append((state, action, reward, next_state, done))
        if len(self.replay_memory) > self.memory_size:
            self.replay_memory.pop(0)
        
    def replay(self):
        if len(self.replay_memory) < self.batch_size:
            return
        samples = np.random.choice(self.replay_memory, self.batch_size)
        states, actions, rewards, next_states, dones = map(np.array, zip(*samples))
        targets = rewards + self.gamma * (np.amax(self.target_network.output.eval(feed_dict={self.target_network.inputs: next_states}), axis=1)) * (1 - dones)
        targets_full = self.q_network.output.eval(feed_dict={self.q_network.inputs: states})
        targets_full[np.arange(self.batch_size), actions] = targets
        self.q_network.optimizer.run(feed_dict={self.q_network.inputs: states, self.q_network.actions: np.eye(self.action_size)[actions], self.q_network.targets: targets_full})
        
        if self.timestep % self.target_update_frequency == 0:
            self.target_network = self.q_network
        self.timestep += 1
        
    def train(self, num_episodes, max_steps):
        for i in range(num_episodes):
            state = env.reset()
            done = False
            total_reward = 0
            for j in range(max_steps):
                action = self.act(state)
                next_state, reward, done, _ = env.step(action)
                self.remember(state, action, reward, next_state, done)
                state = next_state
                total_reward += reward
                self.replay()
                if done:
                    break
            if self.epsilon > self.epsilon_min:
                self.epsilon *= self.epsilon_decay
            print("Episode: {}, Reward: {}".format(i, total_reward))
            
agent = DQNAgent(state_size, action_size, learning_rate, memory_size, batch_size, gamma, epsilon, epsilon_min, epsilon_decay, target_update_frequency)
agent.train(num_episodes, max_steps)

这个代码实现了DQN算法，使用TensorFlow实现了Q-Network和Target-Network。在每个episode中，循环执行以下步骤：

1. 根据当前状态选择一个动作。
2. 执行所选动作并观察下一个状态和奖励。
3. 将当前状态、所选动作、奖励、下一个状态、以及是否终止的标志存储到replay memory中。
4. 从replay memory中随机选择一批经验进行训练，更新Q-Network的参数。
5. 如果需要，更新Target-Network的参数。
6. 如果达到终止条件，则结束循环。

在训练过程中，epsilon值会逐渐减小，从而使得智能体在开始阶段更多的进行探索，然后逐渐转向利用已有知识。同时，也会定期更新Target-Network的参数，以提高算法的稳定性。