用python写一段基于tensflow2.0 的ddpg案例

当然可以！以下是一个基于TensorFlow 2.0的DDPG（Deep Deterministic Policy Gradient）算法的简单示例：

import tensorflow as tf
from tensorflow.keras.layers import Dense
from tensorflow.keras.optimizers import Adam

class Actor(tf.keras.Model):
    def __init__(self, state_dim, action_dim, max_action):
        super(Actor, self).__init__()
        self.hidden1 = Dense(256, activation='relu')
        self.hidden2 = Dense(256, activation='relu')
        self.output_layer = Dense(action_dim, activation='tanh')
        self.max_action = max_action

    def call(self, inputs):
        x = self.hidden1(inputs)
        x = self.hidden2(x)
        x = self.output_layer(x)
        return self.max_action * x

class Critic(tf.keras.Model):
    def __init__(self, state_dim, action_dim):
        super(Critic, self).__init__()
        self.hidden1 = Dense(256, activation='relu')
        self.hidden2 = Dense(256, activation='relu')
        self.output_layer = Dense(1)

    def call(self, state, action):
        x = tf.concat([state, action], axis=1)
        x = self.hidden1(x)
        x = self.hidden2(x)
        return self.output_layer(x)

class DDPG:
    def __init__(self, state_dim, action_dim, max_action):
        self.actor = Actor(state_dim, action_dim, max_action)
        self.critic = Critic(state_dim, action_dim)
        self.target_actor = Actor(state_dim, action_dim, max_action)
        self.target_critic = Critic(state_dim, action_dim)
        self.actor_optimizer = Adam(learning_rate=0.001)
        self.critic_optimizer = Adam(learning_rate=0.001)
        self.tau = 0.005

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

    def train(self, replay_buffer, batch_size=64, gamma=0.99):
        states, actions, next_states, rewards, dones = replay_buffer.sample_batch(batch_size)

        with tf.GradientTape() as tape:
            target_actions = self.target_actor(next_states)
            target_q_values = self.target_critic(next_states, target_actions)
            target_q_values = rewards + gamma * target_q_values * (1 - dones)
            
            q_values = self.critic(states, actions)
            critic_loss = tf.reduce_mean(tf.square(q_values - target_q_values))

        critic_grad = tape.gradient(critic_loss, self.critic.trainable_variables)
        self.critic_optimizer.apply_gradients(zip(critic_grad, self.critic.trainable_variables))

        with tf.GradientTape() as tape:
            new_actions = self.actor(states)
            actor_loss = -tf.reduce_mean(self.critic(states, new_actions))

        actor_grad = tape.gradient(actor_loss, self.actor.trainable_variables)
        self.actor_optimizer.apply_gradients(zip(actor_grad, self.actor.trainable_variables))

        self.update_target_networks()

    def update_target_networks(self):
        for target, source in zip(self.target_actor.trainable_variables, self.actor.trainable_variables):
            target.assign(self.tau * source + (1 - self.tau) * target)
        for target, source in zip(self.target_critic.trainable_variables, self.critic.trainable_variables):
            target.assign(self.tau * source + (1 - self.tau) * target)

# 示例用法
state_dim = 4
action_dim = 2
max_action = 1

ddpg = DDPG(state_dim, action_dim, max_action)

# 训练循环
for episode in range(num_episodes):
    # 执行动作并观察环境
    action = ddpg.get_action(state)
    next_state, reward, done, _ = env.step(action)

    # 将经验存储到回放缓冲区
    replay_buffer.add(state, action, next_state, reward, done)

    # 更新DDPG算法
    ddpg.train(replay_buffer)

    # 更新当前状态
    state = next_state

这只是一个简单的DDPG算法示例，实际应用中可能需要进行更多的调优和改进。希望对你有所帮助！