A Survey on Population-Based Deep Reinforcement Learning

Abstract

Many real-world applications can be described as large-scale games of imperfect information, which require extensive prior domain knowledge, especially in competitive or human–AI cooperation settings. Population-based training methods have become a popular solution to learn robust policies without any prior knowledge, which can generalize to policies of other players or humans. In this survey, we shed light on population-based deep reinforcement learning (PB-DRL) algorithms, their applications, and general frameworks. We introduce several independent subject areas, including naive self-play, fictitious self-play, population-play, evolution-based training methods, and the policy-space response oracle family. These methods provide a variety of approaches to solving multi-agent problems and are useful in designing robust multi-agent reinforcement learning algorithms that can handle complex real-life situations. Finally, we discuss challenges and hot topics in PB-DRL algorithms. We hope that this brief survey can provide guidance and insights for researchers interested in PB-DRL algorithms.