Abstract
Reinforcement learning is one of the most popular models for building agents that deal with the real world but are not distinctly told which actions to perform. In the context of gaming, the application of reinforcement learning thus spans many different categories, from classic arcade games to modern simulations. The aim of this review paper is to present a comprehensive review of reinforcement learning in gaming, its core methods or algorithms, and the results obtained. This paper first lays the groundwork by discussing the basics of reinforcement learning, among which are agents, environments, rewards, and policies. Then we discussed the mathematical framework of reinforcement learning, the Markov decision process, and the Bellman equation. Thereafter, it discusses specific reinforcement learning algorithms that have been successfully implemented in video games. A variety of algorithms has been adapted from the field of reinforcement learning and has shown huge success, like Q-learning, deep Q-networks, and policy gradients. Then we compare the different games and algorithms associated with them and their outcomes. There are also some major challenges with RL in video games, such as computational complexity, environment design, and many more. Finally, the conclusion and future aspects of applications of reinforcement learning in video games are discussed.
Keywords: Artificial Intelligence, Gaming, Machine Learning, Reinforcement Learning, Video Game.