tensorflow DRQN代码

以下是使用TensorFlow实现DRQN（Deep Recurrent Q-Network）的示例代码：

import tensorflow as tf
import numpy as np

class DRQN:
    def __init__(self, state_dim, action_dim, hidden_dim, batch_size, learning_rate, memory_size, max_steps):
        self.state_dim = state_dim
        self.action_dim = action_dim
        self.hidden_dim = hidden_dim
        self.batch_size = batch_size
        self.learning_rate = learning_rate
        self.memory_size = memory_size
        self.max_steps = max_steps
        
        self.replay_memory = []
        self.build_model()
        
        self.sess = tf.Session()
        self.sess.run(tf.global_variables_initializer())
        
    def build_model(self):
        self.state_input = tf.placeholder(tf.float32, [None, self.max_steps, self.state_dim])
        self.action_input = tf.placeholder(tf.int32, [None])
        self.q_target = tf.placeholder(tf.float32, [None])
        
        cell = tf.nn.rnn_cell.BasicLSTMCell(num_units=self.hidden_dim)
        outputs, states = tf.nn.dynamic_rnn(cell, self.state_input, dtype=tf.float32)
        output = outputs[:, -1, :]
        
        w1 = tf.Variable(tf.random_normal([self.hidden_dim, self.action_dim]))
        b1 = tf.Variable(tf.zeros([self.action_dim]))
        
        self.q_value = tf.matmul(output, w1) + b1
        self.predict_action = tf.argmax(self.q_value, axis=1)
        
        action_one_hot = tf.one_hot(self.action_input, self.action_dim)
        q_value_action = tf.reduce_sum(tf.multiply(self.q_value, action_one_hot), axis=1)
        
        self.loss = tf.reduce_mean(tf.square(self.q_target - q_value_action))
        self.optimizer = tf.train.AdamOptimizer(self.learning_rate).minimize(self.loss)
        
    def store_experience(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 choose_action(self, state):
        if np.random.uniform() < 0.1:
            return np.random.choice(self.action_dim)
        else:
            return self.sess.run(self.predict_action, feed_dict={self.state_input: [state]})[0]
        
    def learn(self):
        if len(self.replay_memory) < self.batch_size:
            return
        
        samples = np.random.choice(len(self.replay_memory), self.batch_size, replace=False)
        states = np.zeros([self.batch_size, self.max_steps, self.state_dim])
        actions = np.zeros([self.batch_size])
        rewards = np.zeros([self.batch_size])
        next_states = np.zeros([self.batch_size, self.max_steps, self.state_dim])
        dones = np.zeros([self.batch_size])
        
        for i, sample in enumerate(samples):
            state, action, reward, next_state, done = self.replay_memory[sample]
            states[i] = state
            actions[i] = action
            rewards[i] = reward
            next_states[i] = next_state
            dones[i] = done
            
        q_values_next = self.sess.run(self.q_value, feed_dict={self.state_input: next_states})
        max_q_values_next = np.max(q_values_next, axis=1)
        q_targets = rewards + (1 - dones) * 0.99 * max_q_values_next
        
        self.sess.run(self.optimizer, feed_dict={self.state_input: states, self.action_input: actions, self.q_target: q_targets})

这是一个简单的DRQN实现，它使用LSTM作为循环层，并且处理具有可变长度的序列输入。该模型使用经验回放进行训练，并且在每个时间步上选择动作时使用epsilon-greedy策略。