Challenges and Opportunities for Multi-Agent Reinforcement Learning (COMARL)
COMARL AAAI 2020
March 23-25, 2020
Spring Symposium Series, Stanford University, Palo Alto, California, USA
We live in a multi-agent world and to be successful in that world, intelligent agents, will need to learn to take into account the agency of others. They will need to compete in market places, cooperate in teams, communicate with others, coordinate their plans, and negotiate outcomes. Examples include self-driving cars interacting in traffic, personal assistants acting on behalf of humans and negotiating with other agents, swarms of unmanned aerial vehicles, financial trading systems, robotic teams, and household robots.
There has been a lot of great work on multi-agent reinforcement learning (MARL) in the past decade, but significant challenges remain, including:
- the difficulty of learning an optimal model/policy from a partial signal,
- learning to cooperate/compete in non-stationary environments with distributed, simultaneously learning agents,
- the interplay between abstraction and influence of other agents,
- the exploration vs. exploitation dilemma,
- the scalability and effectiveness of learning algorithms,
- avoiding social dilemmas, and
- learning emergent communication.
The purpose of this symposium is to bring together researchers in multiagent reinforcement learning, but also more widely machine learning and multiagent systems, to explore some of these and other challenges in more detail. The main goal is to broaden the scope of MARL research and to address the fundamental issues that hinder the applicability of MARL for solving complex real world problems.
We aim to organize an active workshop, with many interactive (brainstorm/breakout) sessions. We are hopeful that this will form the basis for ongoing collaborations on these challenges between the attendants and we aim for several position papers as concrete outcomes.
We will also solicit short descriptions, from accepted authors and other participants, of the topic or challenge they would like to work on during the symposium.