dqn路径规划python代码

以下是一个使用DQN（Deep Q-Network）算法实现路径规划的Python代码，可供参考：

import numpy as np
import itertools

from keras.models import Sequential
from keras.layers import Dense
from keras.optimizers import Adam

# 定义DQN算法的类
class DQNAgent:
    def __init__(self, state_size, action_size):
        self.state_size = state_size
        self.action_size = action_size
        self.memory = []
        self.gamma = 0.95    # 折扣因子
        self.epsilon = 1.0   # 探索率
        self.epsilon_decay = 0.995
        self.epsilon_min = 0.01
        self.learning_rate = 0.001
        self.model = self._build_model()

    # 定义神经网络模型
    def _build_model(self):
        model = Sequential()
        model.add(Dense(24, input_dim=self.state_size, activation='relu'))
        model.add(Dense(24, activation='relu'))
        model.add(Dense(self.action_size, activation='linear'))
        model.compile(loss='mse', optimizer=Adam(lr=self.learning_rate))
        return model

    # 将状态/行动/下一个状态/奖励存储到记忆中去
    def remember(self, state, action, next_state, reward):
        self.memory.append((state, action, next_state, reward))

    # 根据给定状态选择行动（通过探索/利用平衡来选择行动）
    def act(self, state):
        if np.random.rand() <= self.epsilon:
            return np.random.choice(self.action_size)
        else:
            return np.argmax(self.model.predict(state)[0])

    # 神经网络训练
    def replay(self, batch_size):
        minibatch = np.random.choice(len(self.memory), batch_size, replace=False)
        for state, action, next_state, reward in minibatch:
            target = self.model.predict(state)
            if next_state is None:
                target[0][action] = reward
            else:
                target[0][action] = reward + self.gamma * np.max(self.model.predict(next_state)[0])
            self.model.fit(state, target, epochs=1, verbose=0)
        if self.epsilon > self.epsilon_min:
            self.epsilon *= self.epsilon_decay

# 定义搜索空间（即迷宫）
grid = [[0, 0, 0, -1], 
        [0, -1, 0, 0], 
        [0, 0, 0, -1], 
        [0, -1, 0, 0], 
        [0, 0, -1, 0]]

# 定义迷宫行列数，起点和终点位置
n_rows = len(grid)
n_cols = len(grid[0])
start = (0, 0)
end = (4, 3)
n_states = n_rows * n_cols

# 将状态编号从0到n_states-1
state2id = dict(zip(itertools.product(range(n_rows), range(n_cols)), range(n_states)))
id2state = {v: k for k, v in state2id.items()}

# 定义DQN算法的参数
batch_size = 32
n_episodes = 2000
n_steps = 100
agent = DQNAgent(2, 4)

# 开始DQN算法
for episode in range(n_episodes):
    state = np.array([start[0], start[1]]).reshape(1, 2)
    for step in range(n_steps):
        action = agent.act(state)
        n_state = None
        reward = 0
        if action == 0: # 上
            if state[0][0] > 0 and grid[state[0][0]-1][state[0][1]] != -1:
                n_state = np.array([state[0][0]-1, state[0][1]]).reshape(1, 2)
        elif action == 1: # 下
            if state[0][0] < n_rows-1 and grid[state[0][0]+1][state[0][1]] != -1:
                n_state = np.array([state[0][0]+1, state[0][1]]).reshape(1, 2)
        elif action == 2: # 左
            if state[0][1] > 0 and grid[state[0][0]][state[0][1]-1] != -1:
                n_state = np.array([state[0][0], state[0][1]-1]).reshape(1, 2)
        else: # 右
            if state[0][1] < n_cols-1 and grid[state[0][0]][state[0][1]+1] != -1:
                n_state = np.array([state[0][0], state[0][1]+1]).reshape(1, 2)
        if n_state is None:
            reward = -1
        elif n_state[0][0] == end[0] and n_state[0][1] == end[1]:
            reward = 1
        else:
            reward = 0
        agent.remember(state, action, n_state, reward)
        state = n_state
        if reward != 0:
            break
    if len(agent.memory) > batch_size:
        agent.replay(batch_size)

该代码实现了一个简单的5x4迷宫路径规划问题，其中-1表示障碍物。DQN算法对于该问题进行了训练，以实现从起点到终点的路径导航。