用pycharm和pytorch写一个基于DDPG算法的无人机降落代码

这是一个比较复杂的项目，需要一些前置知识。以下是一个简单的代码示例，仅供参考，不保证完全正确和可运行，需要根据实际情况进行修改和调整。

首先，我们需要安装PyTorch和OpenAI Gym：

pip install torch gym

然后，我们定义一个无人机降落环境：

import gym
import numpy as np

class LandingEnv(gym.Env):
    def __init__(self):
        self.observation_space = gym.spaces.Box(low=0, high=1, shape=(3,))
        self.action_space = gym.spaces.Box(low=-1, high=1, shape=(1,))
        self.goal = np.array([0.5, 0.5, 0.0])
        self.state = np.array([0.0, 0.0, 1.0])
        self.time_step = 0
        self.max_time_step = 100

    def reset(self):
        self.state = np.array([0.0, 0.0, 1.0])
        self.time_step = 0
        return self.state

    def step(self, action):
        self.time_step += 1
        action = np.clip(action, self.action_space.low, self.action_space.high)
        noise = np.random.normal(0, 0.1)
        next_state = np.clip(self.state + np.array([0.0, 0.0, action[0] + noise]), self.observation_space.low, self.observation_space.high)
        reward = -np.sum(np.abs(next_state - self.goal))
        done = (self.time_step >= self.max_time_step)
        self.state = next_state
        return next_state, reward, done, {}

    def render(self, mode="human"):
        pass

在这个环境中，我们需要让无人机在规定时间内降落到指定高度。环境的观测空间是一个三维向量，表示无人机的高度、速度和加速度。动作空间是一个一维向量，表示无人机的油门输入。奖励函数是负的状态与目标之间的欧氏距离。

接下来，我们定义一个DDPG智能体：

import torch
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
from torch.distributions import Normal

class Actor(nn.Module):
    def __init__(self, state_dim, action_dim, max_action):
        super(Actor, self).__init__()
        self.fc1 = nn.Linear(state_dim, 256)
        self.fc2 = nn.Linear(256, 256)
        self.fc3 = nn.Linear(256, action_dim)
        self.max_action = max_action

    def forward(self, state):
        x = F.relu(self.fc1(state))
        x = F.relu(self.fc2(x))
        x = self.max_action * torch.tanh(self.fc3(x))
        return x

class Critic(nn.Module):
    def __init__(self, state_dim, action_dim):
        super(Critic, self).__init__()
        self.fc1 = nn.Linear(state_dim + action_dim, 256)
        self.fc2 = nn.Linear(256, 256)
        self.fc3 = nn.Linear(256, 1)

    def forward(self, state, action):
        x = torch.cat([state, action], 1)
        x = F.relu(self.fc1(x))
        x = F.relu(self.fc2(x))
        x = self.fc3(x)
        return x

class DDPG(object):
    def __init__(self, state_dim, action_dim, max_action):
        self.actor = Actor(state_dim, action_dim, max_action).cuda()
        self.actor_target = Actor(state_dim, action_dim, max_action).cuda()
        self.actor_target.load_state_dict(self.actor.state_dict())
        self.actor_optimizer = optim.Adam(self.actor.parameters(), lr=1e-3)

        self.critic = Critic(state_dim, action_dim).cuda()
        self.critic_target = Critic(state_dim, action_dim).cuda()
        self.critic_target.load_state_dict(self.critic.state_dict())
        self.critic_optimizer = optim.Adam(self.critic.parameters(), lr=1e-3)

        self.max_action = max_action

    def select_action(self, state):
        state = torch.FloatTensor(state.reshape(1, -1)).cuda()
        action = self.actor(state).cpu().data.numpy().flatten()
        return action

    def train(self, replay_buffer, batch_size=256, discount=0.99, tau=0.001):
        state, action, next_state, reward, done = replay_buffer.sample(batch_size)

        state = torch.FloatTensor(state).cuda()
        action = torch.FloatTensor(action).cuda()
        next_state = torch.FloatTensor(next_state).cuda()
        reward = torch.FloatTensor(reward).cuda()
        done = torch.FloatTensor(done).cuda()

        target_Q = self.critic_target(next_state, self.actor_target(next_state))
        target_Q = reward + ((1 - done) * discount * target_Q).detach()

        current_Q = self.critic(state, action)

        critic_loss = F.mse_loss(current_Q, target_Q)

        self.critic_optimizer.zero_grad()
        critic_loss.backward()
        self.critic_optimizer.step()

        actor_loss = -self.critic(state, self.actor(state)).mean()

        self.actor_optimizer.zero_grad()
        actor_loss.backward()
        self.actor_optimizer.step()

        for param, target_param in zip(self.critic.parameters(), self.critic_target.parameters()):
            target_param.data.copy_(tau * param.data + (1 - tau) * target_param.data)

        for param, target_param in zip(self.actor.parameters(), self.actor_target.parameters()):
            target_param.data.copy_(tau * param.data + (1 - tau) * target_param.data)

在这个DDPG智能体中，我们使用了一个Actor网络和一个Critic网络。Actor网络将状态映射到动作，Critic网络将状态和动作映射到Q值。在训练时，我们使用了一个经验回放缓冲区，每次从中随机采样一批样本进行训练。我们还使用了一些技巧，如目标网络和软更新，以提高训练效果。

最后，我们可以将环境和智能体组合起来进行训练：

env = LandingEnv()
agent = DDPG(state_dim=3, action_dim=1, max_action=1.0)

replay_buffer = ReplayBuffer()

state = env.reset()
while True:
    action = agent.select_action(state)
    next_state, reward, done, _ = env.step(action)
    replay_buffer.add(state, action, next_state, reward, done)
    agent.train(replay_buffer)

    state = next_state

    if done:
        state = env.reset()

在训练结束后，我们可以使用训练好的智能体进行测试：

state = env.reset()
while True:
    action = agent.select_action(state)
    next_state, reward, done, _ = env.step(action)

    state = next_state

    if done:
        state = env.reset()

这就是一个简单的基于DDPG算法的无人机降落代码。需要注意的是，这只是一个示例代码，实际的项目中还需要进行很多修改和调整。