Advancements in Reinforcement Learning - An Overview

Abstract

In the discipline of machine learning, reinforcement learning (RL) is a well-known study area that focuses on sequential decision-making in dynamic contexts. An extensive overview of reinforcement learning is provided in this publication, covering its key concepts, methodologies, and challenges. RL involves mapping situations to actions to maximize the associated rewards, having an agent discovering the behaviours that result in the greatest rewards through trial and error. Key challenges in RL, such as deriving optimal policies, credit assignment, dealing with complex environments, and temporal correlations, are explored. Additionally, the paper delves into the concept of transfer learning, where knowledge is transferred across related tasks to enhance RL performance. The use of transfer learning in single-agent and multi-agent systems is discussed, highlighting methods like instance transfer, representation transfer, and parameter transfer. This paper provides valuable insights into the foundations of RL and its application in solving real-world problems, offering a basis for further research and advancements in this exciting field.