参考引用可知，可借助 Python 构建一个以网格世界（GridWorld）为场景的简易强化学习游戏，并运用 Q - learning 算法，以下是一个简单的基于 Q - learning 算法的网格世界问题代码示例： ```python import numpy as np # 定义网格世界环境 class GridWorld: def __init__(self): self.grid_size = 4 self.start_state = (0, 0) self.goal_state = (3, 3) self.obstacles = [(1, 1), (2, 2)] self.state = self.start_state def reset(self): self.state = self.start_state return self.state def step(self, action): x, y = self.state if action == 0: # 上 x = max(0, x - 1) elif action == 1: # 下 x = min(self.grid_size - 1, x + 1) elif action == 2: # 左 y = max(0, y - 1) elif action == 3: # 右 y = min(self.grid_size - 1, y + 1) elif action == 4: # 不动 pass next_state = (x, y) if next_state in self.obstacles: next_state = self.state reward = -1 if next_state == self.goal_state: reward = 100 self.state = next_state done = (next_state == self.goal_state) return next_state, reward, done # Q - learning 算法实现 def q_learning(env, episodes=1000, alpha=0.1, gamma=0.9, epsilon=0.1): q_table = np.zeros((env.grid_size * env.grid_size, 5)) for episode in range(episodes): state = env.reset() state_index = state[0] * env.grid_size + state[1] done = False while not done: if np.random.uniform(0, 1) < epsilon: action = np.random.randint(0, 5) else: action = np.argmax(q_table[state_index]) next_state, reward, done = env.step(action) next_state_index = next_state[0] * env.grid_size + next_state[1] q_table[state_index, action] = (1 - alpha) * q_table[state_index, action] + \ alpha * (reward + gamma * np.max(q_table[next_state_index])) state_index = next_state_index return q_table # 主程序 if __name__ == "__main__": env = GridWorld() q_table = q_learning(env) print("Q - table:") print(q_table) ``` 上述代码构建了一个简单的网格世界环境，使用 Q - learning 算法让智能体学习在环境中移动并找到从起点到终点的最优路径。