Generating and Adapting to Diverse Ad Hoc Partners in Hanabi

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.In this paper, we focus on adhoc settings with no previous coordination between partners.We introduce a "Bayesian Meta-Agent" that maintains a belief distribution over hypotheses of partner policies.The policies that serve as initial hypotheses are generated using MAP-Elites, to ensure behavioral diversity.We evaluate an "Adaptive" version of the agent, which selects a response policy based on the updated belief distribution and a "Generalist" version, which selects a response based on the uniform prior.In short episodes of 10 games with a consistent partner, the "Adaptive" version outperforms the "Generalist" when the training and evaluation populations are the same.This presents a first step towards an agent that can model its partner and adapt within a time frame that is compatible with human interaction.