强化学习在医疗诊断决策支持中的应用前景

    import numpy as np
    
    # 定义迷宫环境
    class Maze:
    def __init__(self, maze):
        self.maze = maze
        self.start = (0, 0)
        self.goal = (len(maze) - 1, len(maze[0]) - 1)
    
    def get_state(self, position):
        return position
    
    def get_actions(self, state):
        actions = []
        if state[0] > 0 and self.maze[state[0] - 1][state[1]] != 1:
            actions.append('up')
        if state[0] < len(self.maze) - 1 and self.maze[state[0] + 1][state[1]] != 1:
            actions.append('down')
        if state[1] > 0 and self.maze[state[0]][state[1] - 1] != 1:
            actions.append('left')
        if state[1] < len(self.maze[0]) - 1 and self.maze[state[0]][state[1] + 1] != 1:
            actions.append('right')
        return actions
    
    def get_next_state(self, state, action):
        if action == 'up':
            return (state[0] - 1, state[1])
        elif action == 'down':
            return (state[0] + 1, state[1])
        elif action == 'left':
            return (state[0], state[1] - 1)
        elif action == 'right':
            return (state[0], state[1] + 1)
    
    def get_reward(self, state):
        if state == self.goal:
            return 1
        else:
            return 0
    
    # 定义 Q-Learning 智能体
    class QLearningAgent:
    def __init__(self, env, learning_rate=0.1, discount_factor=0.9):
        self.env = env
        self.q_table = {}
        self.learning_rate = learning_rate
        self.discount_factor = discount_factor
    
    def get_action(self, state):
        # 使用 epsilon-greedy 策略选择动作
        if np.random.rand() < 0.1:
            return np.random.choice(self.env.get_actions(state))
        else:
            return max(self.env.get_actions(state), key=lambda action: self.q_table.get((state, action), 0))
    
    def update_q_table(self, state, action, reward, next_state):
        # 更新 Q 值函数
        if (state, action) not in self.q_table:
            self.q_table[(state, action)] = 0
        self.q_table[(state, action)] += self.learning_rate * (reward + self.discount_factor * max(self.q_table.get((next_state, next_action), 0) for next_action in self.env.get_actions(next_state)) - self.q_table[(state, action)])
    
    # 创建迷宫环境和智能体
    maze = Maze([[0, 0, 0],
             [0, 1, 0],
             [0, 0, 0]])
    agent = QLearningAgent(maze)
    
    # 训练智能体
    for episode in range(1000):
    state = maze.start
    while state != maze.goal:
        action = agent.get_action(state)
        next_state = maze.get_next_state(state, action)
        reward = maze.get_reward(next_state)
        agent.update_q_table(state, action, reward, next_state)
        state = next_state
    
    # 测试智能体
    state = maze.start
    while state != maze.goal:
    action = agent.get_action(state)
    next_state = maze.get_next_state(state, action)
    print(f"From {state} to {next_state} by {action}")
    state = next_state
    
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
    
    代码解读