python ddpg

DDPG (Deep Deterministic Policy Gradient) 是一种用于连续动作空间的强化学习算法，它结合了 DQN (Deep Q-Network) 和 DPG (Deterministic Policy Gradient) 的思想。DDPG 可以用于解决连续动作控制问题，例如机器人控制、无人驾驶等。

在 Python 中，你可以使用一些强化学习库来实现 DDPG 算法，例如 TensorFlow、PyTorch 或者 Stable Baselines3。这些库提供了现成的实现和工具，使得实现 DDPG 算法更加简单。

以下是一个使用 TensorFlow 和 Keras 实现 DDPG 算法的简单示例代码：

import numpy as np
import tensorflow as tf
from tensorflow.keras import layers

# 定义 Actor 网络
class Actor(tf.keras.Model):
    def __init__(self, action_dim):
        super(Actor, self).__init__()
        self.fc1 = layers.Dense(256, activation='relu')
        self.fc2 = layers.Dense(256, activation='relu')
        self.fc3 = layers.Dense(action_dim, activation='tanh')

    def call(self, state):
        x = self.fc1(state)
        x = self.fc2(x)
        x = self.fc3(x)
        return x

# 定义 Critic 网络
class Critic(tf.keras.Model):
    def __init__(self):
        super(Critic, self).__init__()
        self.fc1 = layers.Dense(256, activation='relu')
        self.fc2 = layers.Dense(256, activation='relu')
        self.fc3 = layers.Dense(1, activation=None)

    def call(self, state, action):
        x = tf.concat([state, action], axis=-1)
        x = self.fc1(x)
        x = self.fc2(x)
        x = self.fc3(x)
        return x

# 定义 DDPG 算法
class DDPG:
    def __init__(self, state_dim, action_dim):
        self.actor = Actor(action_dim)
        self.critic = Critic()
        self.actor_optimizer = tf.keras.optimizers.Adam(learning_rate=0.001)
        self.critic_optimizer = tf.keras.optimizers.Adam(learning_rate=0.001)

    def get_action(self, state):
        state = tf.expand_dims(state, 0)
        action = self.actor(state)
        return action[0]

    def train(self, states, actions, rewards, next_states, dones, gamma=0.99):
        states = tf.convert_to_tensor(states, dtype=tf.float32)
        actions = tf.convert_to_tensor(actions, dtype=tf.float32)
        rewards = tf.convert_to_tensor(rewards, dtype=tf.float32)
        next_states = tf.convert_to_tensor(next_states, dtype=tf.float32)

        with tf.GradientTape() as tape:
            target_actions = self.actor(next_states)
            target_q_values = self.critic(next_states,