import gradio as gr import numpy as np import random import torch import torch.nn as nn import torch.nn.functional as F from game2048 import Game2048 # 创建游戏实例 game = Game2048(size=4) # 方块颜色映射（根据数字值） TILE_COLORS = { 0: "#cdc1b4", # 空白格子 2: "#eee4da", # 2 4: "#ede0c8", # 4 8: "#f2b179", # 8 16: "#f59563", # 16 32: "#f67c5f", # 32 64: "#f65e3b", # 64 128: "#edcf72", # 128 256: "#edcc61", # 256 512: "#edc850", # 512 1024: "#edc53f", # 1024 2048: "#edc22e", # 2048 4096: "#3c3a32", # 4096+ } # 文本颜色映射（根据背景深浅） TEXT_COLORS = { 0: "#776e65", # 空白格子 2: "#776e65", # 2 4: "#776e65", # 4 8: "#f9f6f2", # 8+ 16: "#f9f6f2", # 16+ 32: "#f9f6f2", # 32+ 64: "#f9f6f2", # 64+ 128: "#f9f6f2", # 128+ 256: "#f9f6f2", # 256+ 512: "#f9f6f2", # 512+ 1024: "#f9f6f2", # 1024+ 2048: "#f9f6f2", # 2048+ 4096: "#f9f6f2", # 4096+ } # 定义DQN网络结构（与训练时相同） class DQN(nn.Module): def __init__(self, input_channels, output_size): super(DQN, self).__init__() self.input_channels = input_channels # 卷积层 self.conv1 = nn.Conv2d(input_channels, 128, kernel_size=3, padding=1) self.conv2 = nn.Conv2d(128, 128, kernel_size=3, padding=1) self.conv3 = nn.Conv2d(128, 128, kernel_size=3, padding=1) # Dueling DQN架构 # 价值流 self.value_conv = nn.Conv2d(128, 4, kernel_size=1) self.value_fc1 = nn.Linear(4 * 4 * 4, 128) self.value_fc2 = nn.Linear(128, 1) # 优势流 self.advantage_conv = nn.Conv2d(128, 16, kernel_size=1) self.advantage_fc1 = nn.Linear(16 * 4 * 4, 128) self.advantage_fc2 = nn.Linear(128, output_size) def forward(self, x): x = F.relu(self.conv1(x)) x = F.relu(self.conv2(x)) x = F.relu(self.conv3(x)) # 价值流 value = F.relu(self.value_conv(x)) value = value.view(value.size(0), -1) value = F.relu(self.value_fc1(value)) value = self.value_fc2(value) # 优势流 advantage = F.relu(self.advantage_conv(x)) advantage = advantage.view(advantage.size(0), -1) advantage = F.relu(self.advantage_fc1(advantage)) advantage = self.advantage_fc2(advantage) # 合并价值流和优势流 q_values = value + advantage - advantage.mean(dim=1, keepdim=True) return q_values # 加载模型 def load_model(model_path): model = DQN(4, 4) # 输入通道4，输出动作4个 try: checkpoint = torch.load(model_path, map_location=torch.device('cpu')) model.load_state_dict(checkpoint['policy_net_state_dict']) model.eval() print("模型加载成功") return model except Exception as e: print(f"模型加载失败: {e}") return None # 尝试加载模型 model_path = "dqn_2048_best_tile.pth" model = load_model(model_path) def render_board(board): html = "
" html += "" for i in range(game.size): html += "" for j in range(game.size): value = board[i][j] color = TILE_COLORS.get(value, "#3c3a32") # 默认深色 text_color = TEXT_COLORS.get(value, "#f9f6f2") # 默认浅色 font_size = "36px" if value < 100 else "30px" if value < 1000 else "24px" html += f""" """ html += "" html += "
{value if value > 0 else ''}
" return html def make_move(direction): """执行移动操作并更新界面""" direction_names = ["上", "右", "下", "左"] # 执行移动 new_board, game_over = game.move(direction) # 渲染棋盘 board_html = render_board(new_board) # 更新状态信息 status = f"移动方向: {direction_names[direction]}" status += f"
当前分数: {game.score}" status += f"
最大方块: {np.max(game.board)}" if game.game_over: status += "

游戏结束!
" status += f"
最终分数: {game.score}" return board_html, status def reset_game(): """重置游戏""" global game game = Game2048(size=4) board = game.reset() # 渲染棋盘 board_html = render_board(board) # 初始状态信息 status = "游戏已重置!" status += f"
当前分数: {game.score}" status += f"
最大方块: {np.max(game.board)}" return board_html, status def ai_move(): """使用AI模型进行一步移动""" if model is None: return render_board(game.board), "错误: 未加载AI模型" # 获取当前状态 state = game.get_state() # 获取有效移动 valid_moves = game.get_valid_moves() if not valid_moves: return render_board(game.board), "游戏结束! 没有有效移动" # 转换状态为模型输入 state_tensor = torch.tensor(state, dtype=torch.float).unsqueeze(0) # 模型预测 with torch.no_grad(): q_values = model(state_tensor).numpy().flatten() # 只考虑有效动作 valid_q_values = np.full(4, -np.inf) for move in valid_moves: valid_q_values[move] = q_values[move] # 选择最佳动作 action = np.argmax(valid_q_values) # 执行移动 direction_names = ["上", "右", "下", "左"] new_board, game_over = game.move(action) # 渲染棋盘 board_html = render_board(new_board) # 更新状态信息 status = f"AI移动方向: {direction_names[action]}" status += f"
当前分数: {game.score}" status += f"
最大方块: {np.max(game.board)}" if game.game_over: status += "

游戏结束!
" status += f"
最终分数: {game.score}" return board_html, status # 创建Gradio界面 with gr.Blocks(title="2048游戏", theme="soft") as demo: gr.Markdown("# 🎮 2048游戏") gr.Markdown("使用方向键或下方的按钮移动方块，相同数字的方块相撞时会合并!") with gr.Row(): with gr.Column(scale=2): board_html = gr.HTML(render_board(game.board)) with gr.Row(visible=False): status_display = gr.HTML("当前分数: 0
最大方块: 2") with gr.Column(): gr.Markdown("## 手动操作") with gr.Row(): gr.Button("上 ↑", elem_id="up-btn").click( fn=lambda: make_move(0), outputs=[board_html, status_display] ) gr.Button("左 ←", elem_id="left-btn").click( fn=lambda: make_move(3), outputs=[board_html, status_display] ) with gr.Row(): gr.Button("下 ↓", elem_id="down-btn").click( fn=lambda: make_move(2), outputs=[board_html, status_display] ) gr.Button("右 →", elem_id="right-btn").click( fn=lambda: make_move(1), outputs=[board_html, status_display] ) with gr.Row(): gr.Button("🔄 重置游戏", elem_id="reset-btn").click( fn=reset_game, outputs=[board_html, status_display] ) with gr.Row(): status_display = gr.HTML("当前分数: 0
最大方块: 2") with gr.Column(): gr.Markdown("## AI操作") gr.Button("🤖 AI移动一步", elem_id="ai-btn").click( fn=ai_move, outputs=[board_html, status_display] ) gr.Markdown("基于DQN神经网络提供支持") # 添加键盘快捷键支持 demo.load( fn=None, inputs=None, outputs=None, js="""() => { document.addEventListener('keydown', function(e) { if (e.key === 'ArrowUp') { document.getElementById('up-btn').click(); } else if (e.key === 'ArrowRight') { document.getElementById('right-btn').click(); } else if (e.key === 'ArrowDown') { document.getElementById('down-btn').click(); } else if (e.key === 'ArrowLeft') { document.getElementById('left-btn').click(); } else if (e.key === 'r' || e.key === 'R') { document.getElementById('reset-btn').click(); } else if (e.key === 'a' || e.key === 'A') { document.getElementById('ai-btn').click(); } }); }""" ) gr.Markdown("### 📚 使用说明") gr.Markdown("1. 使用方向键或下方的按钮移动方块。") gr.Markdown("2. 相同数字的方块相撞时会合并。") gr.Markdown("3. 快捷键说明：上/下/左/右键移动方块，R键重置游戏，A键AI移动一步。") gr.Markdown("4. 点击 '🤖 AI移动一步' 按钮可以使用AI模型进行一步移动。") gr.Markdown("5. 游戏结束后，会显示最终分数和最大方块。") # 启动界面 if __name__ == "__main__": demo.launch(share=True)