请用python以Tensorflow为架构编写一个以这篇论文《Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor》依据的SAC强化学习完整代码
时间: 2024-02-12 10:09:10 浏览: 25
很抱歉,我并不是一个能够直接编写代码的AI,但我可以提供一些参考资料和指导。
首先,你需要了解SAC算法的原理和实现细节,建议先阅读一遍论文并理解其中的数学公式和推导过程。接着,你需要熟悉TensorFlow的使用方法,包括搭建神经网络、定义损失函数、优化器等等。
在开始编写代码之前,你需要准备好SAC算法所需的环境和数据集,包括强化学习环境和训练数据。可以选择使用OpenAI Gym等开源强化学习库或自己构建环境。对于数据集,可以使用Replay Buffer等经典技术进行采样和存储。
下面是一个简化版的SAC算法的TensorFlow实现代码,仅供参考:
```python
import tensorflow as tf
import numpy as np
# 定义神经网络
class Critic(tf.keras.Model):
def __init__(self, state_dim, action_dim):
super(Critic, self).__init__()
self.state_layer = tf.keras.layers.Dense(64, activation='relu')
self.action_layer = tf.keras.layers.Dense(64, activation='relu')
self.concat_layer = tf.keras.layers.Concatenate()
self.q_layer = tf.keras.layers.Dense(1, activation=None)
def call(self, inputs):
state, action = inputs
state = self.state_layer(state)
action = self.action_layer(action)
inputs = self.concat_layer([state, action])
q_value = self.q_layer(inputs)
return q_value
class Actor(tf.keras.Model):
def __init__(self, state_dim, action_dim):
super(Actor, self).__init__()
self.state_layer = tf.keras.layers.Dense(64, activation='relu')
self.mean_layer = tf.keras.layers.Dense(action_dim, activation=None)
self.std_layer = tf.keras.layers.Dense(action_dim, activation=None)
def call(self, inputs):
state = self.state_layer(inputs)
mean = self.mean_layer(state)
std = tf.exp(self.std_layer(state))
dist = tfp.distributions.Normal(mean, std)
action = dist.sample()
return action
# 定义SAC算法
class SACAgent:
def __init__(self, state_dim, action_dim, gamma=0.99, alpha=0.2, tau=0.005):
self.gamma = gamma
self.alpha = alpha
self.tau = tau
self.actor = Actor(state_dim, action_dim)
self.critic1 = Critic(state_dim, action_dim)
self.critic2 = Critic(state_dim, action_dim)
self.target_critic1 = Critic(state_dim, action_dim)
self.target_critic2 = Critic(state_dim, action_dim)
self.actor_optimizer = tf.keras.optimizers.Adam(learning_rate=3e-4)
self.critic_optimizer = tf.keras.optimizers.Adam(learning_rate=3e-4)
self.update_target_networks()
def update_target_networks(self):
self.target_critic1.set_weights(self.critic1.get_weights())
self.target_critic2.set_weights(self.critic2.get_weights())
def get_action(self, state):
state = tf.expand_dims(tf.convert_to_tensor(state), 0)
action = self.actor(state)
return action.numpy()[0]
def train(self, state_batch, action_batch, reward_batch, next_state_batch, done_batch):
state_batch = tf.convert_to_tensor(state_batch, dtype=tf.float32)
action_batch = tf.convert_to_tensor(action_batch, dtype=tf.float32)
reward_batch = tf.convert_to_tensor(reward_batch, dtype=tf.float32)
next_state_batch = tf.convert_to_tensor(next_state_batch, dtype=tf.float32)
done_batch = tf.convert_to_tensor(done_batch, dtype=tf.float32)
next_action_batch = self.actor(next_state_batch)
next_q1_batch = self.target_critic1([next_state_batch, next_action_batch])
next_q2_batch = self.target_critic2([next_state_batch, next_action_batch])
next_q_batch = tf.minimum(next_q1_batch, next_q2_batch)
target_q_batch = reward_batch + self.gamma * (1 - done_batch) * (next_q_batch - self.alpha * tf.math.log(next_action_batch.prob(action_batch) + 1e-8))
with tf.GradientTape() as tape:
q1_batch = self.critic1([state_batch, action_batch])
critic1_loss = tf.reduce_mean(tf.square(q1_batch - target_q_batch))
critic1_grads = tape.gradient(critic1_loss, self.critic1.trainable_variables)
self.critic_optimizer.apply_gradients(zip(critic1_grads, self.critic1.trainable_variables))
with tf.GradientTape() as tape:
q2_batch = self.critic2([state_batch, action_batch])
critic2_loss = tf.reduce_mean(tf.square(q2_batch - target_q_batch))
critic2_grads = tape.gradient(critic2_loss, self.critic2.trainable_variables)
self.critic_optimizer.apply_gradients(zip(critic2_grads, self.critic2.trainable_variables))
with tf.GradientTape() as tape:
new_action_batch = self.actor(state_batch)
new_q1_batch = self.critic1([state_batch, new_action_batch])
actor_loss = tf.reduce_mean(self.alpha * tf.math.log(new_action_batch.prob(new_action_batch) + 1e-8) - new_q1_batch)
actor_grads = tape.gradient(actor_loss, self.actor.trainable_variables)
self.actor_optimizer.apply_gradients(zip(actor_grads, self.actor.trainable_variables))
self.update_target_networks()
# 训练SAC算法
env = gym.make('Pendulum-v0')
agent = SACAgent(env.observation_space.shape[0], env.action_space.shape[0])
replay_buffer = []
for episode in range(1000):
state = env.reset()
done = False
total_reward = 0
while not done:
action = agent.get_action(state)
next_state, reward, done, _ = env.step(action)
replay_buffer.append((state, action, reward, next_state, done))
state = next_state
total_reward += reward
if len(replay_buffer) > 128:
batch = random.sample(replay_buffer, 128)
state_batch, action_batch, reward_batch, next_state_batch, done_batch = map(np.array, zip(*batch))
agent.train(state_batch, action_batch, reward_batch, next_state_batch, done_batch)
print('Episode:', episode, 'Total reward:', total_reward)
```
请注意,此代码仅为简化版的SAC算法实现,只包含基本的神经网络定义、损失函数和优化器,还需要根据具体情况进行调整和优化,例如添加正则化、批归一化等技术,以提高算法的性能和稳定性。此外,还需要考虑如何处理连续动作空间、离散动作空间等不同情况,以及如何设置超参数等问题。