A Comprehensive Review of Reinforcement Learning

Abstract

Reinforcement Learning (RL) is a paradigm of machine learning in which an agent learns optimal behavior through trial-and-error interactions with an environment guided by feedback in the form of rewards. This survey provides a comprehensive overview of Reinforcement Learning (RL), beginning with fundamental concepts (Markov decision processes, policies, value functions, and reward signals) and progressing to the prominent categories of RL algorithms: value-based, policy-based, actor-critic architectures, and model-based. We categorize and explain these families and highlight representative algorithms (from Q-learning and Deep Q-Networks to policy gradient methods like REINFORCE, proximal policy optimization, and more). We then summarize recent advancements, including the deep RL revolution that combines neural networks with RL to solve high-dimensional problems, the emergence of offline RL for learning from fixed datasets, progress in multi-agent RL for complex, competitive, and cooperative systems, and hierarchical RL for temporal abstraction. Key real-world applications are reviewed in domains such as robotics (where RL enables autonomous control and manipulation), game playing (where RL has achieved human- and superhuman-level performance in video games and board games), finance (for algorithmic trading and decision-making under uncertainty), and healthcare (for treatment planning and medical decision support).