System Design Perspective for Human-Level Agents Using Deep Reinforcement Learning: A Survey

Abstract

Reinforcement learning (RL) has distinguished itself as a prominent learning method to augment the efficacy of autonomous systems. Recent advances in deep learning studies have complemented existing RL methods and led to a crucial breakthrough in the effort of applying RL to automation and robotics. Artificial agents based on deep RL can take selective and intelligent actions comparable with those of a human to maximize the feedback reward from the interactive environment. In this paper, we survey recent developments in the literature regarding deep RL methods for building human-level agents. As a result, prominent studies that involve modeling every aspect of a human-level agent will be examined. We also provide an overview of constructing a framework for prospective autonomous systems. Moreover, various toolkits and frameworks are suggested to facilitate the development of deep RL methods. Finally, we open a discussion that potentially raises a range of future research directions in deep RL.