写一个基于Python的DQN路径规划

好的，以下是一个基于Python的DQN路径规划的程序示例：

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

# 定义状态空间
states = [(i, j, k) for i in range(10) for j in range(10) for k in range(4)]
num_states = len(states)

# 定义动作空间
actions = [(0, 1), (0, -1), (1, 0), (-1, 0)]
num_actions = len(actions)

# 定义奖励函数
def reward_func(state, action):
    x, y, z = state
    dx, dy = action
    next_x, next_y = x + dx, y + dy
    if next_x < 0 or next_x >= 10 or next_y < 0 or next_y >= 10:
        return -1  # 撞到边界，给予负奖励
    if next_x == 7 and next_y == 7 and z == 3:
        return 1  # 到达目标位置，给予正奖励
    if next_x == 5 and next_y == 5 and z == 2:
        return 0.5  # 经过特定位置，给予部分奖励
    return 0  # 其他情况，给予零奖励

# 定义DQN模型
def build_model():
    model = Sequential()
    model.add(Dense(64, input_dim=num_states, activation='relu'))
    model.add(Dense(64, activation='relu'))
    model.add(Dense(num_actions, activation='linear'))
    model.compile(loss='mse', optimizer=Adam(lr=0.001))
    return model

# 定义经验回放缓冲区
class ReplayBuffer:
    def __init__(self, buffer_size):
        self.buffer_size = buffer_size
        self.buffer = []

    def add(self, experience):
        if len(self.buffer) + len(experience) >= self.buffer_size:
            self.buffer[0:(len(experience) + len(self.buffer)) - self.buffer_size] = []
        self.buffer.extend(experience)

    def sample(self, size):
        return np.reshape(np.array(random.sample(self.buffer, size)), [size, 5])

# 定义DQN智能体
class DQNAgent:
    def __init__(self):
        self.model = build_model()
        self.target_model = build_model()
        self.buffer = ReplayBuffer(50000)
        self.gamma = 0.99
        self.epsilon = 1.0
        self.epsilon_min = 0.01
        self.epsilon_decay = 0.995
        self.batch_size = 64
        self.update_target_model()

    def update_target_model(self):
        self.target_model.set_weights(self.model.get_weights())

    def choose_action(self, state):
        if np.random.rand() <= self.epsilon:
            return np.random.randint(num_actions)
        act_values = self.model.predict(np.reshape(state, [1, num_states]))
        return np.argmax(act_values[0])

    def learn(self):
        if len(self.buffer.buffer) < self.batch_size:
            return
        minibatch = self.buffer.sample(self.batch_size)
        states = np.array([i[0] for i in minibatch])
        actions = np.array([i[1] for i in minibatch])
        rewards = np.array([i[2] for i in minibatch])
        next_states = np.array([i[3] for i in minibatch])
        dones = np.array([i[4] for i in minibatch])
        targets = self.model.predict(states)
        q_next = self.target_model.predict(next_states)
        targets[range(self.batch_size), actions] = rewards + self.gamma * np.max(q_next, axis=1) * (1 - dones)
        self.model.fit(states, targets, epochs=1, verbose=0)

    def decay_epsilon(self):
        if self.epsilon > self.epsilon_min:
            self.epsilon *= self.epsilon_decay

# 初始化智能体
agent = DQNAgent()
num_episodes = 5000

# 训练模型
for episode in range(num_episodes):
    state = (0, 0, 0)
    total_reward = 0
    while True:
        # 选择动作
        action = agent.choose_action(state)
        # 更新状态并计算奖励
        next_state = (state[0] + actions[action][0], state[1] + actions[action][1], (state[2] + 1) % 4)
        reward = reward_func(state, actions[action])
        agent.buffer.add([(state, action, reward, next_state, False)])
        total_reward += reward
        # 学习
        agent.learn()
        # 更新状态
        state = next_state
        if state == (7, 7, 3):
            agent.buffer.add([(state, 0, 0, None, True)])
            break
    # 打印结果
    print("第{}轮训练的总奖励为：{}".format(episode + 1, total_reward))
    # 更新目标模型和epsilon
    agent.update_target_model()
    agent.decay_epsilon()

# 测试模型
state = (0, 0, 0)
path = [(0, 0)]
while state != (7, 7, 3):
    action = np.argmax(agent.model.predict(np.reshape(state, [1, num_states]))[0])
    next_state = (state[0] + actions[action][0], state[1] + actions[action][1], (state[2] + 1) % 4)
    path.append((next_state[0], next_state[1]))
    state = next_state

# 输出结果
print("最短路径为：", path)

这个程序使用DQN算法来实现三维路径规划，在训练结束后，输出从起点到达目标点的最短路径。在实践中，需要根据具体应用场景进行选择和调整。可以使用Python中的Keras和numpy等库来实现DQN算法。