Reinforcement Learning: An Overview
arXiv:2412.05265v48 citations
Originality Synthesis-oriented
AI Analysis
It serves as an introductory resource for researchers and practitioners in AI and ML, offering a broad survey of existing techniques rather than incremental or novel contributions.
The manuscript provides a comprehensive overview of the field of reinforcement learning and sequential decision making, covering various methods and topics without presenting new experimental results or specific numerical findings.
This manuscript gives a big-picture, up-to-date overview of the field of (deep) reinforcement learning and sequential decision making, covering value-based methods, policy-based methods, model-based methods, multi-agent RL, LLMs and RL, and various other topics (e.g., offline RL, hierarchical RL, intrinsic reward).