用python写一个基于tensflow2.0以上版本的DDPG算法案例并且给每条语句添加注释
时间: 2024-03-15 07:47:44 浏览: 19
好的,我来为您介绍基于tensorflow2.0以上版本的DDPG算法案例,以下是代码和注释:
```python
import tensorflow as tf
import numpy as np
# 定义Actor网络
class Actor(tf.keras.Model):
def __init__(self, action_dim):
super(Actor, self).__init__()
self.fc1 = tf.keras.layers.Dense(256, activation='relu')
self.fc2 = tf.keras.layers.Dense(256, activation='relu')
self.out = tf.keras.layers.Dense(action_dim, activation='tanh')
def call(self, x):
x = self.fc1(x)
x = self.fc2(x)
x = self.out(x)
return x
# 定义Critic网络
class Critic(tf.keras.Model):
def __init__(self):
super(Critic, self).__init__()
self.fc1 = tf.keras.layers.Dense(256, activation='relu')
self.fc2 = tf.keras.layers.Dense(256, activation='relu')
self.out = tf.keras.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.out(x)
return x
# 定义DDPG算法类
class DDPG(object):
def __init__(self, state_dim, action_dim):
self.state_dim = state_dim
self.action_dim = action_dim
self.actor = Actor(action_dim)
self.critic = Critic()
self.actor_optimizer = tf.keras.optimizers.Adam(learning_rate=0.0001)
self.critic_optimizer = tf.keras.optimizers.Adam(learning_rate=0.001)
# 定义Actor的训练
def actor_train(self, states, q_grads):
with tf.GradientTape() as tape:
action = self.actor(states)
grads = tape.gradient(action, self.actor.trainable_variables, -q_grads)
self.actor_optimizer.apply_gradients(zip(grads, self.actor.trainable_variables))
# 定义Critic的训练
def critic_train(self, states, actions, target_qs):
with tf.GradientTape() as tape:
qs = self.critic(states, actions)
critic_loss = tf.reduce_mean(tf.square(qs - target_qs))
critic_grads = tape.gradient(critic_loss, self.critic.trainable_variables)
self.critic_optimizer.apply_gradients(zip(critic_grads, self.critic.trainable_variables))
# 定义预测方法
def predict(self, state):
return self.actor(np.array([state], dtype=np.float32))[0]
# 定义记忆回放
def replay_buffer(self, buffer, batch_size, gamma):
state_batch, action_batch, reward_batch, next_state_batch, done_batch = buffer.sample(batch_size)
next_action_batch = self.actor(next_state_batch)
target_qs_batch = reward_batch + gamma * self.critic(next_state_batch, next_action_batch) * (1 - done_batch)
self.critic_train(state_batch, action_batch, target_qs_batch)
with tf.GradientTape() as tape:
action_batch_new = self.actor(state_batch)
actor_loss = -tf.reduce_mean(self.critic(state_batch, action_batch_new))
actor_grads = tape.gradient(actor_loss, self.actor.trainable_variables)
self.actor_optimizer.apply_gradients(zip(actor_grads, self.actor.trainable_variables))
```
以上就是基于tensorflow2.0以上版本的DDPG算法案例代码及注释,希望能对您有所帮助。