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Equivariant Networks for Zero-Shot Coordination
Darius Muglich · Christian Schroeder de Witt · Elise van der Pol · Shimon Whiteson · Jakob Foerster

Tue Nov 29 09:30 AM -- 11:00 AM (PST) @ Hall J #111

Successful coordination in Dec-POMDPs requires agents to adopt robust strategies and interpretable styles of play for their partner. A common failure mode is symmetry breaking, when agents arbitrarily converge on one out of many equivalent but mutually incompatible policies. Commonly these examples include partial observability, e.g. waving your right hand vs. left hand to convey a covert message. In this paper, we present a novel equivariant network architecture for use in Dec-POMDPs that prevents the agent from learning policies which break symmetries, doing so more effectively than prior methods. Our method also acts as a "coordination-improvement operator" for generic, pre-trained policies, and thus may be applied at test-time in conjunction with any self-play algorithm. We provide theoretical guarantees of our work and test on the AI benchmark task of Hanabi, where we demonstrate our methods outperforming other symmetry-aware baselines in zero-shot coordination, as well as able to improve the coordination ability of a variety of pre-trained policies. In particular, we show our method can be used to improve on the state of the art for zero-shot coordination on the Hanabi benchmark.

Author Information

Darius Muglich (University of British Columbia)
Christian Schroeder de Witt (University of Oxford)

I am a 4th-year PhD student conducting fundamental algorithmic research in deep multi-agent reinforcement learning and climate change. My supervision is jointly between Prof. Shimon Whiteson (WhiRL - see my [profile](http://whirl.cs.ox.ac.uk/member/christian-schroeder-de-witt/)) and Prof. Philip Torr (Torr Vision Group).

Elise van der Pol (Microsoft Research)
Shimon Whiteson (Oxford University)
Jakob Foerster (University of Oxford)

Jakob Foerster received a CIFAR AI chair in 2019 and is starting as an Assistant Professor at the University of Toronto and the Vector Institute in the academic year 20/21. During his PhD at the University of Oxford, he helped bring deep multi-agent reinforcement learning to the forefront of AI research and interned at Google Brain, OpenAI, and DeepMind. He has since been working as a research scientist at Facebook AI Research in California, where he will continue advancing the field up to his move to Toronto. He was the lead organizer of the first Emergent Communication (EmeCom) workshop at NeurIPS in 2017, which he has helped organize ever since.

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