A major challenge of multiagent reinforcement learning (MARL) is the curse of multiagents,
where the size of the joint action space scales exponentially with the number of agents. This …

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 many practical applications, such as poker, chess, drug interdiction, cybersecurity, and
national defense, players often have adversarial stances, ie, the selfish actions of each …

Modern reinforcement learning (RL) commonly engages practical problems with large state
spaces, where function approximation must be deployed to approximate either the value …

The mechanism design theory can be applied not only in the economy but also in many
fields, such as politics and military affairs, which has important practical and strategic …

We study a repeated information design problem faced by an informed sender who tries to
influence the behavior of a self-interested receiver. We consider settings where the receiver …

We introduce a new approach for computing optimal equilibria via learning in games. It
applies to extensive-form settings with any number of players, including mechanism design …

We introduce DREAM, a deep reinforcement learning algorithm that finds optimal strategies
in imperfect-information games with multiple agents. Formally, DREAM converges to a Nash …

This paper resolves the open question of designing near-optimal algorithms for learning
imperfect-information extensive-form games from bandit feedback. We present the first line …

No-regret learners seek to minimize the difference between the loss they cumulated through
the actions they played, and the loss they would have cumulated in hindsight had they …