A Comprehensive Evaluation and Comparison of Enhanced Learning Methods

Abstract

This paper provides a comprehensive evaluation and comparison of current reinforcement learning methods. By analyzing the strengths and weaknesses of the main methods, such as value function-based, strategy gradient-based, and value and strategy-based methods, the differences in their performances on standard problems and the applicable scenarios are explored. Meanwhile, other methods such as Monte Carlo Tree Search (MCTS) and evolutionary methods are also briefly introduced. Through the research and analysis in this paper, it provides reference and guidance for choosing appropriate reinforcement learning methods, and promotes the development and application of reinforcement learning techniques in practical applications.