Reinforcement Learning Algorithms: A Comprehensive Guide

This article provides a comprehensive overview of reinforcement learning algorithms, starting from fundamental value and policy iteration, progressing to Monte Carlo methods, temporal difference learning, value-based methods, and policy gradient methods. It delves into advanced algorithms like Deep Q-Networks (DQN), TRPO, and PPO. The article uses a problem-solution approach, systematically explaining the core ideas and improvements of various algorithms, making it a valuable reference for the reinforcement learning field.