Multi-agent reinforcement learning has made substantial empirical progresses in solving
games with a large number of players. However, theoretically, the best known sample …

In the empirical approach to game-theoretic analysis (EGTA), the model of the game comes
not from declarative representation, but is derived by interrogation of a procedural …

Model-based reinforcement learning (RL), which finds an optimal policy after establishing an
empirical model, has long been recognized as one of the cornerstones of RL. It is especially …

We consider the problem of decentralized multi-agent reinforcement learning in Markov
games. A fundamental question is whether there exist algorithms that, when run …

Although recent work in AI has made great progress in solving large, zero-sum, extensive-
form games, the underlying assumption in most past work is that the parameters of the game …

For a constant ϵ, we prove a (N) lower bound on the (randomized) communication
complexity of ϵ-Nash equilibrium in two-player N x N games. For n-player binary-action …

This paper investigates the evaluation of learned multiagent strategies in the incomplete
information setting, which plays a critical role in ranking and training of agents. Traditionally …

In the first-order query model for zero-sum $ K\times K $ matrix games, players observe the
expected pay-offs for all their possible actions under the randomized action played by their …

We study the query complexity of approximate notions of Nash equilibrium in games with a
large number of players n. Our main result states that for n-player binary-action games and …

Nash equilibrium is a central concept in game theory. Several Nash solvers exist, yet none
scale to normal-form games with many actions and many players, especially those with …