本项目旨在探索强化学习技术在经典游戏《超级玛丽》中的应用，通过训练一个智能代理来自主导航并完成游戏关卡。我们采用了深度Q网络（DQN）和双深度Q网络（DDQN）等先进的强化学习算法，结合神经网络，使得代理能够学习如何在游戏世界中生存并获得高分。 项目特点 强化学习实践：本项目是强化学习理论与实践的结合，展示了如何将强化学习算法应用于实际问题中。 深度学习集成：通过集成深度学习模型，我们的智能代理能够处理复杂的游戏环境并做出决策。 环境优化：我们对游戏环境进行了优化，包括状态预处理和奖励设计，以提高学习效率和代理性能。 可视化工具：项目包含了训练过程的可视化工具，帮助开发者和研究人员理解代理的学习进度和行为策略。
☆18Mar 25, 2026Updated this week
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