python 代码实现GCN-DDPG
时间: 2023-10-14 14:29:49 浏览: 212
GCN-DDPG是一种基于图卷积神经网络和深度确定性策略梯度(DDPG)的强化学习算法。以下是一个简单的Python代码实现,仅供参考:
```python
import tensorflow as tf
import numpy as np
import gym
import random
from collections import deque
# 定义超参数
EPISODES = 5000
BATCH_SIZE = 64
GAMMA = 0.99
TAU = 0.001
LR_ACTOR = 0.0001
LR_CRITIC = 0.001
# 定义图卷积神经网络层
class GraphConvolution(tf.keras.layers.Layer):
def __init__(self, output_dim):
super(GraphConvolution, self).__init__()
self.output_dim = output_dim
def build(self, input_shape):
self.kernel = self.add_weight(name='kernel',
shape=(input_shape[1], self.output_dim),
initializer='glorot_uniform',
trainable=True)
def call(self, inputs):
features, adj = inputs
output = tf.matmul(adj, features)
output = tf.matmul(output, self.kernel)
return tf.nn.relu(output)
# 定义Actor模型
class Actor(tf.keras.Model):
def __init__(self, state_shape, action_shape):
super(Actor, self).__init__()
self.fc1 = tf.keras.layers.Dense(64, activation='relu')
self.fc2 = tf.keras.layers.Dense(64, activation='relu')
self.fc3 = tf.keras.layers.Dense(action_shape[0], activation='tanh')
def call(self, state):
x = self.fc1(state)
x = self.fc2(x)
x = self.fc3(x)
return x
# 定义Critic模型
class Critic(tf.keras.Model):
def __init__(self, state_shape, action_shape):
super(Critic, self).__init__()
self.fc1 = tf.keras.layers.Dense(64, activation='relu')
self.fc2 = tf.keras.layers.Dense(64, activation='relu')
self.fc3 = tf.keras.layers.Dense(action_shape[0], activation='linear')
def call(self, inputs):
state, action = inputs
x = tf.concat([state, action], axis=-1)
x = self.fc1(x)
x = self.fc2(x)
x = self.fc3(x)
return x
# 定义Replay Buffer
class ReplayBuffer:
def __init__(self, buffer_size):
self.buffer_size = buffer_size
self.buffer = deque(maxlen=buffer_size)
def add(self, state, action, reward, next_state, done):
experience = (state, action, reward, next_state, done)
self.buffer.append(experience)
def sample(self, batch_size):
batch = random.sample(self.buffer, batch_size)
state_batch = np.array([experience[0] for experience in batch])
action_batch = np.array([experience[1] for experience in batch])
reward_batch = np.array([experience[2] for experience in batch])
next_state_batch = np.array([experience[3] for experience in batch])
done_batch = np.array([experience[4] for experience in batch])
return state_batch, action_batch, reward_batch, next_state_batch, done_batch
def size(self):
return len(self.buffer)
# 定义环境
env = gym.make('Pendulum-v0')
state_shape = env.observation_space.shape
action_shape = env.action_space.shape
# 初始化Actor和Critic模型
actor = Actor(state_shape, action_shape)
critic = Critic(state_shape, action_shape)
actor_target = Actor(state_shape, action_shape)
critic_target = Critic(state_shape, action_shape)
actor_optimizer = tf.keras.optimizers.Adam(learning_rate=LR_ACTOR)
critic_optimizer = tf.keras.optimizers.Adam(learning_rate=LR_CRITIC)
# 将Actor和Critic的参数复制到对应的目标网络
actor_target.set_weights(actor.get_weights())
critic_target.set_weights(critic.get_weights())
# 定义Replay Buffer
replay_buffer = ReplayBuffer(10000)
# 定义训练函数
@tf.function
def train_actor(state):
with tf.GradientTape() as tape:
action = actor(state)
q_value = critic([state, action])
loss = -tf.reduce_mean(q_value)
gradients = tape.gradient(loss, actor.trainable_variables)
actor_optimizer.apply_gradients(zip(gradients, actor.trainable_variables))
@tf.function
def train_critic(state, action, reward, next_state, done):
with tf.GradientTape() as tape:
target_action = actor_target(next_state)
target_q_value = critic_target([next_state, target_action])
y = reward + (1 - done) * GAMMA * target_q_value
q_value = critic([state, action])
td_error = y - q_value
loss = tf.reduce_mean(tf.square(td_error))
gradients = tape.gradient(loss, critic.trainable_variables)
critic_optimizer.apply_gradients(zip(gradients, critic.trainable_variables))
# 开始训练
for episode in range(EPISODES):
state = env.reset()
episode_reward = 0
while True:
action = actor(np.expand_dims(state, axis=0))[0]
action += np.random.normal(0, 0.1, size=action_shape[0])
action = np.clip(action, -1.0, 1.0)
next_state, reward, done, _ = env.step(action)
replay_buffer.add(state, action, reward, next_state, done)
episode_reward += reward
if replay_buffer.size() >= BATCH_SIZE:
state_batch, action_batch, reward_batch, next_state_batch, done_batch = replay_buffer.sample(BATCH_SIZE)
train_critic(state_batch, action_batch, reward_batch, next_state_batch, done_batch)
train_actor(state_batch)
# 软更新Actor和Critic的目标网络
for t, e in zip(actor_target.trainable_variables, actor.trainable_variables):
t.assign(t * (1 - TAU) + e * TAU)
for t, e in zip(critic_target.trainable_variables, critic.trainable_variables):
t.assign(t * (1 - TAU) + e * TAU)
state = next_state
if done:
break
print('Episode: {}, Reward: {}'.format(episode, episode_reward))
```
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