This paper provides a comprehensive introduction to reinforcement learning (RL), a subfield of artificial intelligence. It explains core RL concepts like states, actions, policies, and rewards, detailing their roles in agent-environment interaction. The paper categorizes and describes various RL algorithms, including value-based, policy-based, and hybrid methods, such as Q-learning and Proximal Policy Optimization. Furthermore, it offers a curated list of resources for continued learning in RL, including books, online courses, and communities. Finally, the paper concludes by summarizing the key takeaways and providing references for deeper study.
