This paper comprehensively surveys reinforcement learning (RL) algorithms, categorizing them into value-based, policy-based, and actor-critic methods. It analyzes numerous algorithms, from foundational tabular methods to advanced deep RL techniques, examining their strengths, weaknesses, scalability, and sample efficiency. The survey explores various applications of these algorithms across diverse domains, including robotics, game playing, and network optimization. Specific algorithm variations and their implementations in research papers are discussed, providing practical insights for researchers and practitioners. Finally, the paper concludes by summarizing key findings and suggesting future research directions.

https://arxiv.org/pdf/2411.18892 ...