Mastering Reinforcement Learning: Foundations, Algorithms, and Real-World Applications

• DOI: 10.31219/osf.io/bg79j_v2
• Publication Date: 2025-02-20T20:57:46Z
• Authors: Xinyuan Song; Keyu Chen; Ziqian Bi; Qian Niu; Junyu Liu; Benji Peng; Sen Zhang; Ming Liu; Ming Li; Xuanhe Pan; Jiawei Xu; Jinlang Wang; Pohsun Feng; Zichen Yuan; Li Zhang; Yan Zhong

Reinforcement Learning (RL) is a distinct branch of machine learning focused on how agents should take actions in an environment to maximize cumulative rewards. Unlike supervised learning, which relies labeled datasets, RL driven by the agent's interactions with its environment, optimal behaviors through trial and error. The agent learns make decisions performing certain receiving rewards or penalties return. goal learn policy that maximizes reward over time.