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Melting Pot Contest

Rakshit Trivedi · Akbir Khan · Jesse Clifton · Lewis Hammond · John Agapiou · Edgar DueƱez-Guzman · Jayd Matyas · Dylan Hadfield-Menell · Joel Leibo

Room 357


Multi-agent AI research promises a path to develop human-like and human-compatible intelligent technologies that complement the solipsistic view of other approaches, which mostly do not consider interactions between agents. We propose a Cooperative AI contest based on the Melting Pot framework. At its core, Melting Pot provides an evaluation protocol that measures generalization to novel social partners in a set of canonical test scenarios. There exist several benchmarks, challenges, and contests aimed at spurring research on cooperation in multi-agent learning. Melting Pot expands and generalizes these previous efforts in several ways: (1) it focuses on mixed-motive games, (as opposed to purely cooperative or competitive games); (2) it enables testing generalizability of agent cooperation to previously unseen coplayers; (3) it consists of a suite of multiple environments rather than a single one; and (4) it includes games with larger numbers of players (> 7). These properties make it an accessible while also challenging framework for multi-agent AI research. For this contest, we invite multi-agent reinforcement learning solutions that focus on driving cooperation between interacting agents in the Melting Pot environments and generalize to new situations beyond training. A scoring mechanism based on metrics representative of cooperative intelligence will be used to measure success of the solutions. We believe that Melting Pot can serve as a clear benchmark to drive progress on Cooperative AI, as it focuses specifically on evaluating social intelligence of both groups and individuals. As an overarching goal, we are excited in assessing the implications of current definitions of cooperative intelligence on resulting solution approaches and studying the emerging behaviors of proposed solutions to inform future research directions in Cooperative AI.

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