Generating and Adapting to Diverse Ad-Hoc Cooperation Agents in Hanabi

• URL: http://arxiv.org/abs/2004.13710v3
• Date: Mon, 29 Aug 2022 20:05:13 GMT
• Title: Generating and Adapting to Diverse Ad-Hoc Cooperation Agents in Hanabi
• Authors: Rodrigo Canaan, Xianbo Gao, Julian Togelius, Andy Nealen and Stefan Menzel
• Abstract summary: In Hanabi, coordinated groups of players can leverage pre-established conventions to great effect, but playing in an ad-hoc setting requires agents to adapt to its partner's strategies with no previous coordination. This paper proposes Quality Diversity algorithms as a promising class of algorithms to generate diverse populations for this purpose. We also postulate that agents can benefit from a diverse population during training and implement a simple "meta-strategy" for adapting to an agent's perceived behavioral niche.
• Score: 4.777698073163644
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Hanabi is a cooperative game that brings the problem of modeling other players to the forefront. In this game, coordinated groups of players can leverage pre-established conventions to great effect, but playing in an ad-hoc setting requires agents to adapt to its partner's strategies with no previous coordination. Evaluating an agent in this setting requires a diverse population of potential partners, but so far, the behavioral diversity of agents has not been considered in a systematic way. This paper proposes Quality Diversity algorithms as a promising class of algorithms to generate diverse populations for this purpose, and generates a population of diverse Hanabi agents using MAP-Elites. We also postulate that agents can benefit from a diverse population during training and implement a simple "meta-strategy" for adapting to an agent's perceived behavioral niche. We show this meta-strategy can work better than generalist strategies even outside the population it was trained with if its partner's behavioral niche can be correctly inferred, but in practice a partner's behavior depends and interferes with the meta-agent's own behavior, suggesting an avenue for future research in characterizing another agent's behavior during gameplay.

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