用python写一个MODRQN的代码

MODRQN是一种深度强化学习算法，它结合了多步时间差分算法和RQN（Recurrent Q-Network）的思想，可以用于解决强化学习中的决策任务。下面是一个用Python实现MODRQN算法的代码示例：

import tensorflow as tf
import numpy as np

class MODRQN:
    def __init__(self, state_dim, action_dim, num_steps, num_layers, hidden_dim):
        self.state_dim = state_dim
        self.action_dim = action_dim
        self.num_steps = num_steps
        self.num_layers = num_layers
        self.hidden_dim = hidden_dim
        
        self.inputs = tf.placeholder(tf.float32, [None, self.num_steps, self.state_dim])
        self.actions = tf.placeholder(tf.int32, [None, self.num_steps])
        self.targets = tf.placeholder(tf.float32, [None])
        
        # 定义模型
        lstm_cells = [tf.nn.rnn_cell.BasicLSTMCell(self.hidden_dim) for _ in range(num_layers)]
        lstm = tf.nn.rnn_cell.MultiRNNCell(lstm_cells)
        initial_state = lstm.zero_state(tf.shape(self.inputs)[0], tf.float32)
        outputs, _ = tf.nn.dynamic_rnn(lstm, self.inputs, initial_state=initial_state, dtype=tf.float32)
        outputs = tf.transpose(outputs, [1, 0, 2])
        last_output = tf.gather(outputs, self.num_steps - 1)
        
        # 计算价值函数
        self.w = tf.Variable(tf.random_normal([self.hidden_dim, self.action_dim]))
        self.b = tf.Variable(tf.zeros([self.action_dim]))
        q = tf.matmul(last_output, self.w) + self.b
        
        # 计算损失函数
        mask = tf.one_hot(self.actions, self.action_dim)
        q_masked = tf.reduce_sum(q * mask, axis=1)
        self.loss = tf.reduce_mean(tf.square(self.targets - q_masked))
        self.optimizer = tf.train.AdamOptimizer().minimize(self.loss)
        
        self.sess = tf.Session()
        self.sess.run(tf.global_variables_initializer())
    
    def train(self, inputs, actions, targets):
        self.sess.run(self.optimizer, feed_dict={self.inputs: inputs, self.actions: actions, self.targets: targets})
    
    def predict(self, inputs):
        return self.sess.run(q, feed_dict={self.inputs: inputs})

这个代码定义了一个MODRQN类，其构造函数需要传入状态维度state_dim、动作维度action_dim、多步时间差分算法的时间步数num_steps、RQN的层数num_layers和隐藏维度hidden_dim。在训练时，需要调用train方法，传入输入inputs、动作actions和目标值targets，即可更新模型参数。在预测时，需要调用predict方法，传入输入inputs，即可得到模型的输出。