DDPG的经验回放机制怎么改进成有权重控制的，用tensorflow2.0写

要改进DDPG的经验回放机制，使其具有权重控制，可以使用优先经验回放（Prioritized Experience Replay）算法。下面是一个使用TensorFlow 2.0实现的示例代码：

首先，导入所需的库：

import numpy as np
import tensorflow as tf
from collections import deque

定义一个经验回放缓存类，用于存储和采样经验数据：

class ReplayBuffer:
    def __init__(self, buffer_size):
        self.buffer_size = buffer_size
        self.buffer = deque(maxlen=buffer_size)
        self.priorities = deque(maxlen=buffer_size)
    
    def add(self, state, action, reward, next_state, done):
        experience = (state, action, reward, next_state, done)
        self.buffer.append(experience)
        self.priorities.append(max(self.priorities, default=1))
    
    def sample(self, batch_size, priority_scale):
        priorities = np.array(self.priorities)
        probabilities = priorities ** priority_scale / np.sum(priorities ** priority_scale)
        
        indices = np.random.choice(len(self.buffer), batch_size, p=probabilities)
        samples = [self.buffer[idx] for idx in indices]
        
        total = len(self.buffer)
        weights = (total * probabilities[indices]) ** -beta
        
        states = np.array([sample[0] for sample in samples])
        actions = np.array([sample[1] for sample in samples])
        rewards = np.array([sample[2] for sample in samples])
        next_states = np.array([sample[3] for sample in samples])
        dones = np.array([sample[4] for sample in samples])
        
        return states, actions, rewards, next_states, dones, indices, weights
    
    def update_priorities(self, indices, errors, epsilon=1e-6):
        priorities = np.abs(errors) + epsilon
        for i, idx in enumerate(indices):
            self.priorities[idx] = priorities[i]

接下来，定义一个DDPG代理类，包含经验回放缓存的实例：

class DDPGAgent:
    def __init__(self, state_dim, action_dim, buffer_size, priority_scale):
        self.state_dim = state_dim
        self.action_dim = action_dim
        self.buffer = ReplayBuffer(buffer_size)
        self.priority_scale = priority_scale
        
        # 其他DDPG代理的初始化代码...
    
    def add_experience(self, state, action, reward, next_state, done):
        self.buffer.add(state, action, reward, next_state, done)
    
    def train(self, batch_size):
        states, actions, rewards, next_states, dones, indices, weights = self.buffer.sample(batch_size, self.priority_scale)
        
        # 计算TD误差并执行梯度下降更新
        
        td_errors = # 计算TD误差的代码
        
        self.buffer.update_priorities(indices, td_errors)
        
        # 其他DDPG训练的代码...

在训练过程中，您可以使用`add_experience`方法将经验添加到缓存中。然后，通过调用`train`方法来执行DDPG的训练，其中会从经验回放缓存中采样一批经验数据，并根据TD误差更新经验的优先级。

请注意，上述代码中的TD误差的计算部分需要根据您的具体问题和DDPG网络的架构进行适当修改。

希望这个示例代码能够帮助您改进DDPG的经验回放机制并添加权重控制！