Algorithms for Multi-Agent Reinforcement Learning in Complex Environments
Multi-agent learning is an approach to solving sequential interactive decision problems, in which multiple autonomous agents learn through repeated interaction how to solve problems together. This includes agents working in a team to collaboratively accomplish tasks, as well as agents in competitive scenarios with conflicting goals. Reinforcement learning has emerged as one of the principal methodologies used in multi-agent learning, and a recent tutorial by Albrecht and Stone provides a basic introduction .
The core problem in multi-agent learning is that the environment is non-stationary from the perspective of individual agents: each agent learns about and adapts to the environment which includes other agents who, likewise, are continually adapting their behaviours. Several approaches have been proposed to tackle this non-stationarity, including modelling the behaviours of other agents, learning to communicate, and centralised training architectures. However, non-stationarity remains a significant open challenge, which is further complicated by the need to scale to complex domains and to deal with partial observability of the environment .
The goal of this project is to develop novel algorithms for highly-efficient multi-agent reinforcement learning in complex environments. Examples of potential evaluation domains include competitive games (e.g. Robocup 2D/3D soccer, Starcraft 2), autonomous vehicles in dense city traffic, multi-robot warehouse management systems, and autonomous wireless networks such as in DARPA's Spectrum Collaboration Challenge.
 S. Albrecht, P. Stone (2017). Multiagent Learning: Foundations and Recent Trends. Tutorial at IJCAI'17 conference. http://www.cs.utexas.edu/~larg/ijcai17_tutorial
 G. Papoudakis, F. Christianos, A. Rahman, S. Albrecht (2019). Dealing with Non-Stationarity in Multi-Agent Deep Reinforcement Learning. https://arxiv.org/abs/1906.04737