A Comparative Study of Reinforcement Learning Algorithms for Game AI"
Abstract
Reinforcement learning has shown remarkable promise in the field of game AI, enabling agents to learn from their interactions with the environment. This paper presents a comprehensive comparative study of reinforcement learning algorithms to evaluate their performance in game environments. We analyze the suitability of popular algorithms such as Q-learning, Deep Q-Networks (DQN), Proximal Policy Optimization (PPO), and Actor-Critic methods in diverse game scenarios.
The research aims to identify which algorithms excel in different game types, assessing factors like training stability, sample efficiency, and adaptability. Through extensive experimentation, we provide insights into the strengths and weaknesses of these algorithms, aiding game developers in selecting the most appropriate method for their specific gaming applications.
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References
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