Diverse Conventions for Human-AI Collaboration
Bidipta Sarkar Andy Shih Dorsa Sadigh
Conventions are crucial for strong performance in cooperative multi-agent
games, because they allow players to coordinate on a shared strategy without
explicit communication. Unfortunately, standard multi-agent reinforcement
learning techniques, such as self-play, converge to conventions that are
arbitrary and non-diverse, leading to poor generalization when interacting with
new partners. In this work, we present a technique for generating diverse
conventions by (1) maximizing their rewards during self-play, while (2)
minimizing their rewards when playing with previously discovered conventions
(cross-play), stimulating conventions to be semantically different. To ensure
that learned policies act in good faith despite the adversarial optimization of
cross-play, we introduce \emph{mixed-play}, where an initial state is randomly
generated by sampling self-play and cross-play transitions and the player
learns to maximize the self-play reward from this initial state. We analyze the
benefits of our technique on various multi-agent collaborative games, including
Overcooked, and find that our technique can adapt to the conventions of humans,
surpassing human-level performance when paired with real users.