强化学习DQN做动态障碍物避障

Question

有没有用DQN做动态障碍物避障的哇，有、……偿，找一份code

Answer 1

我可以提供一份基于 Python 的 DQN 代码来实现动态障碍物避障。这里是一个简单的例子：

import random
import gym
import numpy as np
from keras.models import Sequential
from keras.layers import Dense
from keras.optimizers import Adam

# 创建环境
env = gym.make('MountainCar-v0')

# 定义DQN模型
model = Sequential()
model.add(Dense(24, input_shape=(env.observation_space.shape[0],), activation='relu'))
model.add(Dense(48, activation='relu'))
model.add(Dense(env.action_space.n, activation='linear'))
model.compile(loss='mse', optimizer=Adam(lr=0.001))

# 设置训练参数
episodes = 1000
epsilon = 1.0
epsilon_min = 0.01
epsilon_decay = 0.995

# 开始训练
for episode in range(episodes):
    state = env.reset()
    state = np.reshape(state, [1, env.observation_space.shape[0]])
    for step in range(env.spec.timestep_limit):
        env.render()
        if np.random.rand() < epsilon:
            action = env.action_space.sample()
        else:
            action = np.argmax(model.predict(state))
        next_state, reward, done, _ = env.step(action)
        next_state = np.reshape(next_state, [1, env.observation_space.shape[0]])
        target = reward + 0.95 * np.amax(model.predict(next_state))
        target_f = model.predict(state)
        target_f[0][action] = target
        model.fit(state, target_f, epochs=1, verbose=0)
        state = next_state
        if done:
            break
    epsilon = max(epsilon_min, epsilon * epsilon_decay)

env.close()

这是一个简化版本，你需要调整网络结构和超参数以适应你的问题，也可以使用更高级的DQN算法（如Double DQN,Dueling DQN）来提升效果。