DQN雷达干扰决策pytorch
时间: 2023-08-31 13:08:46 浏览: 75
以下是使用PyTorch实现DQN雷达干扰决策的示例代码:
```python
import torch
import torch.nn as nn
import torch.optim as optim
import numpy as np
# 定义DQN模型
class DQN(nn.Module):
def __init__(self, state_dim, action_dim):
super(DQN, self).__init__()
self.fc1 = nn.Linear(state_dim, 64)
self.fc2 = nn.Linear(64, 64)
self.fc3 = nn.Linear(64, action_dim)
def forward(self, x):
x = torch.relu(self.fc1(x))
x = torch.relu(self.fc2(x))
return self.fc3(x)
# 定义DQN Agent
class DQNAgent:
def __init__(self, state_dim, action_dim):
self.state_dim = state_dim
self.action_dim = action_dim
self.model = DQN(state_dim, action_dim)
self.target_model = DQN(state_dim, action_dim)
self.optimizer = optim.Adam(self.model.parameters(), lr=0.001)
self.memory = []
def act(self, state):
state = torch.from_numpy(state).float().unsqueeze(0)
q_values = self.model(state)
return np.argmax(q_values.detach().numpy())
def remember(self, state, action, reward, next_state, done):
self.memory.append((state, action, reward, next_state, done))
def replay(self, batch_size):
if len(self.memory) < batch_size:
return
batch = random.sample(self.memory, batch_size)
states, actions, rewards, next_states, dones = zip(*batch)
states = torch.tensor(states).float()
actions = torch.tensor(actions).long()
rewards = torch.tensor(rewards).float()
next_states = torch.tensor(next_states).float()
dones = torch.tensor(dones).float()
q_values = self.model(states)
next_q_values = self.target_model(next_states)
max_next_q_values = torch.max(next_q_values, dim=1)[0]
targets = rewards + (1 - dones) * max_next_q_values
q_values = q_values.gather(1, actions.unsqueeze(1)).squeeze(1)
loss = nn.MSELoss()(q_values, targets)
self.optimizer.zero_grad()
loss.backward()
self.optimizer.step()
def update_target_model(self):
self.target_model.load_state_dict(self.model.state_dict())
# 定义环境和训练过程
state_dim = 4 # 状态空间维度
action_dim = 2 # 动作空间维度
env = RadarEnv() # 自定义雷达环境类
agent = DQNAgent(state_dim, action_dim)
episodes = 1000 # 训练的总回合数
batch_size = 32 # 每次训练的样本批次大小
for episode in range(episodes):
state = env.reset()
done = False
total_reward = 0
while not done:
action = agent.act(state)
next_state, reward, done = env.step(action)
agent.remember(state, action, reward, next_state, done)
state = next_state
total_reward += reward
agent.replay(batch_size)
agent.update_target_model()
print('Episode: {}, Total Reward: {}'.format(episode, total_reward))
```
请注意,以上代码只是一个简单的框架,你需要根据具体的雷达干扰决策问题进行相应的环境定义和数据处理。同时,你可能需要自定义雷达环境类和相应的状态、动作表示方式。