Enhancing Entertainment Through Reinforcement Learning: Applications in Gaming

Reinforcement learning (RL) has become a central paradigm for developing intelligent gaming agents capable of learning optimal strategies through interaction with dynamic environments. This review provides a structured analysis of RL applications in gaming, beginning with core principles such as Markov decision processes, value functions, and policy optimization. It distinguishes between classical value‐based methods like Q‐learning and deep RL approaches, including deep Q‐networks (DQNs), policy‐gradient algorithms such as REINFORCE and proximal policy optimization (PPO), and actor–critic frameworks. The manuscript further contextualizes foundational algorithms including DDPG, TRPO, and soft actor–critic to present a broader methodological landscape. Through a comprehensive literature survey, major achievements such as AlphaGo and OpenAI Five are examined, alongside persistent challenges including reward design, scalability, and training stability. A conceptual framework illustrating RL integration within gaming pipelines is also presented. Overall, this study clarifies algorithmic distinctions and highlights future research directions for robust and adaptive game intelligence systems.