Reinforcement Learning (RL) has been widely used in many applications, particularly in gaming, which serves as an excellent training ground for AI models. Google DeepMind has pioneered innovations in this field, employing reinforcement learning algorithms, including model-based, model-free, and deep Q-network approaches, to create advanced AI models such as AlphaGo, AlphaGo Zero, and MuZero. AlphaGo, the initial model, integrates supervised learning and reinforcement learning to master the game of Go, surpassing professional human players. AlphaGo Zero refines this approach by eliminating reliance on human gameplay data, instead utilizing self-play for enhanced learning efficiency. MuZero further extends these advancements by learning the underlying dynamics of game environments without explicit knowledge of the rules, achieving adaptability across various games, including complex Atari games. This paper reviews the significance of reinforcement learning applications in Atari and strategy-based games, analyzing these three models, their key innovations, training processes, challenges encountered, and improvements made. Additionally, we discuss advancements in the field of gaming, including MiniZero and multi-agent models, highlighting future directions and emerging AI models from Google DeepMind.

翻译：强化学习（RL）已在诸多领域得到广泛应用，尤其在游戏领域，其为人工智能模型提供了绝佳的训练场。谷歌DeepMind在该领域开创了多项创新，通过运用包括基于模型、无模型以及深度Q网络在内的强化学习算法，开发了诸如AlphaGo、AlphaGo Zero和MuZero等先进人工智能模型。AlphaGo作为初始模型，融合了监督学习与强化学习以精通围棋游戏，其水平超越了人类职业棋手。AlphaGo Zero对此方法进行了改进，摒弃了对人类对弈数据的依赖，转而通过自我对弈来提升学习效率。MuZero进一步拓展了这些进展，它无需显式规则知识即可学习游戏环境的内在动态，从而实现了在包括复杂雅达利游戏在内的多种游戏中的适应性。本文综述了强化学习在雅达利及基于策略游戏中的应用意义，分析了上述三种模型、其关键创新、训练过程、遇到的挑战以及所做的改进。此外，我们还探讨了游戏领域的进展，包括MiniZero与多智能体模型，并展望了未来方向以及谷歌DeepMind正在涌现的新人工智能模型。