Recent developments in deep reinforcement learning are concerned with creating decision-making agents which can perform well in various complex domains. A particular approach which has received increasing attention is multi-agent reinforcement learning, in which multiple agents learn concurrently to coordinate their actions. In such multi-agent environments, additional learning problems arise due to the continually changing decision-making policies of agents. This paper surveys recent works that address the non-stationarity problem in multi-agent deep reinforcement learning. The surveyed methods range from modifications in the training procedure, such as centralized training, to learning representations of the opponent's policy, meta-learning, communication, and decentralized learning. The survey concludes with a list of open problems and possible lines of future research.
@article{arxiv.1906.04737,
title = {Dealing with Non-Stationarity in Multi-Agent Deep Reinforcement Learning},
author = {Georgios Papoudakis and Filippos Christianos and Arrasy Rahman and Stefano V. Albrecht},
journal= {arXiv preprint arXiv:1906.04737},
year = {2019}
}