ddpg算法实现车辆避障

### DDPG算法实现车辆避障 #### 1. 环境设置与依赖安装 为了运行DDPG算法来实现实时车辆避障，首先需要准备环境并安装必要的库。 ```bash pip install gym torch numpy matplotlib ``` #### 2. 定义环境 定义一个模拟环境用于测试和训练。此环境中包含障碍物以及一辆可以移动的小车。该部分可以通过自定义`gym.Env`类完成。 ```python import gym from gym import spaces import numpy as np import random import math class VehicleEnv(gym.Env): metadata = {'render.modes': ['human']} def __init__(self, goal_velocity=0): super(VehicleEnv).__init__() self.max_speed = 1.0 self.min_position = -1.2 self.max_position = 0.6 # 动作空间 [-1, 1], 表示加速度范围 self.action_space = spaces.Box(low=-1.0, high=1.0, shape=(1,), dtype=np.float32) # 观测空间 [position, velocity] low = np.array([self.min_position, -self.max_speed]) high = np.array([self.max_position, self.max_speed]) self.observation_space = spaces.Box(low, high, dtype=np.float32) def reset(self): """重置环境""" pass def step(self, action): """执行一步操作""" position, velocity = self.state force = min(max(action[0], -1), 1) * 0.0015 velocity += force if (velocity > self.max_speed): velocity = self.max_speed if (velocity < -self.max_speed): velocity = -self.max_speed position += velocity if (position > self.max_position): position = self.max_position if (position < self.min_position): position = self.min_position done = bool(position >= self.goal_position and velocity >= self.goal_velocity) reward = -1.0 if not done else 0.0 self.state = (np.array([position, velocity])) return np.array(self.state), reward, done, {} def render(self, mode='human'): """渲染当前状态""" pass ``` 上述代码片段仅提供了一个简化版的环境框架[^2]。 #### 3. 构建DDPG模型 接下来构建DDPG所需的Actor（策略网络）和Critic（价值函数评估网络）。这里使用PyTorch作为深度学习框架。 ```python import torch import torch.nn.functional as F from torch import nn class Actor(nn.Module): def __init__(self, state_dim, action_dim, max_action): super(Actor, self).__init__() self.l1 = nn.Linear(state_dim, 400) self.l2 = nn.Linear(400, 300) self.l3 = nn.Linear(300, action_dim) self.max_action = max_action def forward(self, x): x = F.relu(self.l1(x)) x = F.relu(self.l2(x)) x = self.max_action * torch.tanh(self.l3(x)) return x class Critic(nn.Module): def __init__(self, state_dim, action_dim): super(Critic, self).__init__() self.l1 = nn.Linear(state_dim + action_dim, 400) self.l2 = nn.Linear(400, 300) self.l3 = nn.Linear(300, 1) def forward(self, x, u): xu = torch.cat([x, u], dim=1) x1 = F.relu(self.l1(xu)) x1 = F.relu(self.l2(x1)) x1 = self.l3(x1) return x1 ``` 这部分展示了如何创建两个神经网络以分别代表actor和critic角色[^3]。 #### 4. 训练过程概览 实际应用中还需要考虑经验回放缓冲区、噪声生成器等因素，并按照一定周期更新target network权重。完整的训练流程涉及多个组件之间的协调工作，在此不再赘述具体细节[^1]。