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Hanabi is a cooperative game that challenges exist-ing AI techniques due toits focus on modeling the mental states ofother players to interpret andpredict their behavior. While thereare agents that can achieve near-perfectscores in the game byagreeing on some shared strategy, comparatively littleprogresshas been made in ad-hoc cooperation settings, where partnersandstrategies are not known in advance. In this paper, we showthat agents trainedthrough self-play using the popular RainbowDQN architecture fail to cooperatewell with simple rule-basedagents that were not seen during training and,conversely, whenthese agents are trained to play with any individualrule-basedagent, or even a mix of these agents, they fail to achievegoodself-play scores.

Evaluating the Rainbow DQN Agent in Hanabi with Unseen Partners