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例子：

import torch
import torch.nn as nn
import torch.optim as optim
import random
import matplotlib.pyplot as plt# 🏁 迷宫环境（5×5）
class MazeEnv:def __init__(self, size=5):self.size = sizeself.state = (0, 0)  # 起点self.goal = (size-1, size-1)  # 终点self.actions = [(0,1), (0,-1), (1,0), (-1,0)]  # 右、左、下、上def reset(self):self.state = (0, 0)  # 重置起点return self.statedef step(self, action):dx, dy = self.actions[action]x, y = self.statenx, ny = max(0, min(self.size-1, x+dx)), max(0, min(self.size-1, y+dy))reward = 1 if (nx, ny) == self.goal else -0.1done = (nx, ny) == self.goalself.state = (nx, ny)return (nx, ny), reward, done# 🤖 GRU 策略网络
class GRUPolicy(nn.Module):def __init__(self, input_size, hidden_size, output_size):super(GRUPolicy, self).__init__()self.gru = nn.GRU(input_size, hidden_size, batch_first=True)self.fc = nn.Linear(hidden_size, output_size)def forward(self, x, hidden):out, hidden = self.gru(x, hidden)out = self.fc(out[:, -1, :])  # 只取最后时间步return out, hidden# 🎯 训练参数
env = MazeEnv(size=5)
policy = GRUPolicy(input_size=2, hidden_size=16, output_size=4)
optimizer = optim.Adam(policy.parameters(), lr=0.01)
loss_fn = nn.CrossEntropyLoss()# 🎓 训练
num_episodes = 500
epsilon = 1.0  # 初始的ε值，控制探索的概率
epsilon_min = 0.01  # 最小ε值
epsilon_decay = 0.995  # ε衰减率
best_path = []  # 用于存储最佳路径for episode in range(num_episodes):state = env.reset()hidden = torch.zeros(1, 1, 16)  # GRU 初始状态states, actions, rewards = [], [], []logits_list = []  for _ in range(20):  # 最多 20 步state_tensor = torch.tensor([[state[0], state[1]]], dtype=torch.float32).unsqueeze(0)logits, hidden = policy(state_tensor, hidden)logits_list.append(logits)# ε-greedy 策略if random.random() < epsilon:action = random.choice(range(4))  # 随机选择动作else:action = torch.argmax(logits, dim=1).item()  # 选择最大值对应的动作next_state, reward, done = env.step(action)states.append(state)actions.append(action)rewards.append(reward)if done:print(f"Episode {episode} - Reached Goal!")# 找到最优路径best_path = states + [next_state]  # 当前 episode 的路径breakstate = next_state# 计算损失logits = torch.cat(logits_list, dim=0)  # (T, 4)action_tensor = torch.tensor(actions, dtype=torch.long)  # (T,)loss = loss_fn(logits, action_tensor)  optimizer.zero_grad()loss.backward()optimizer.step()# 衰减 εepsilon = max(epsilon_min, epsilon * epsilon_decay)if episode % 100 == 0:print(f"Episode {episode}, Loss: {loss.item():.4f}, Epsilon: {epsilon:.4f}")# 🧐 确保 best_path 已经记录
if len(best_path) == 0:print("No path found during training.")
else:print(f"Best path: {best_path}")# 🚀 测试路径（只绘制最佳路径）
fig, ax = plt.subplots(figsize=(6,6))# 初始化迷宫图
maze = [[0 for _ in range(5)] for _ in range(5)]  # 5×5 迷宫
ax.imshow(maze, cmap="coolwarm", origin="upper")# 画网格
ax.set_xticks(range(5))
ax.set_yticks(range(5))
ax.grid(True, color="black", linewidth=0.5)# 画出最佳路径（红色）
for (x, y) in best_path:ax.add_patch(plt.Rectangle((y, x), 1, 1, color="red", alpha=0.8))# 画起点和终点
ax.text(0, 0, "S", ha="center", va="center", fontsize=14, color="white", fontweight="bold")
ax.text(4, 4, "G", ha="center", va="center", fontsize=14, color="white", fontweight="bold")plt.title("GRU RL Agent - Best Path")
plt.show()