  Recent breakthroughs in AI for multi-agent games like Go, Poker, and Dota,
have seen great strides in recent years. Yet none of these games address the
real-life challenge of cooperation in the presence of unknown and uncertain
teammates. This challenge is a key game mechanism in hidden role games. Here we
develop the DeepRole algorithm, a multi-agent reinforcement learning agent that
we test on The Resistance: Avalon, the most popular hidden role game. DeepRole
combines counterfactual regret minimization (CFR) with deep value networks
trained through self-play. Our algorithm integrates deductive reasoning into
vector-form CFR to reason about joint beliefs and deduce partially observable
actions. We augment deep value networks with constraints that yield
interpretable representations of win probabilities. These innovations enable
DeepRole to scale to the full Avalon game. Empirical game-theoretic methods
show that DeepRole outperforms other hand-crafted and learned agents in
five-player Avalon. DeepRole played with and against human players on the web
in hybrid human-agent teams. We find that DeepRole outperforms human players as
both a cooperator and a competitor.
