A Survey on Deep Reinforcement Learning-based Approaches for Adaptation and Generalization

TL;DR

This survey reviews recent deep reinforcement learning methods focused on improving adaptation and generalization, highlighting key approaches, challenges, and future research directions for broad real-world applicability.

Contribution

It provides a comprehensive overview of recent DRL approaches for adaptation and generalization, and discusses future research directions to enhance their real-world effectiveness.

Findings

Summarizes recent DRL methods for adaptation and generalization

Identifies key challenges in applying DRL broadly

Suggests future research directions for improved DRL performance

Abstract

Deep Reinforcement Learning (DRL) aims to create intelligent agents that can learn to solve complex problems efficiently in a real-world environment. Typically, two learning goals: adaptation and generalization are used for baselining DRL algorithm's performance on different tasks and domains. This paper presents a survey on the recent developments in DRL-based approaches for adaptation and generalization. We begin by formulating these goals in the context of task and domain. Then we review the recent works under those approaches and discuss future research directions through which DRL algorithms' adaptability and generalizability can be enhanced and potentially make them applicable to a broad range of real-world problems.