We give a simple and computationally efficient algorithm that, for any constant ε> 0, obtains ε
T-swap regret within only T=(n) rounds; this is an exponential improvement compared to the …

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 …

A wide array of modern machine learning applications–from adversarial models to multi-
agent reinforcement learning–can be formulated as non-cooperative games whose Nash …

Equilibrium problems in Bayesian auction games can be described as systems of differential
equations. Depending on the model assumptions, these equations might be such that we do …

In repeated games, such as auctions, players typically use learning algorithms to choose
their actions. The use of such autonomous learning agents has become widespread on …

The framework of multi-agent learning explores the dynamics of how an agent's strategies
evolve in response to the evolving strategies of other agents. Of particular interest is whether …

Exploration is an integral part of learning dynamics which allows algorithms to search a
space of solutions. When many algorithms simultaneously explore, this can lead to counter …

We start from the fundamental premise that any physical interaction can be interpreted as a
game. To demonstrate this, we draw upon the free energy principle and the theory of …

We present a learning algorithm for players to converge to their stationary policies in a
general sum stochastic sequential Stackelberg game. The algorithm is a two time scale …

During 2023, two interesting results were proven about the limit behavior of game dynamics:
First, it was shown that there is a game for which no dynamics converges to the Nash …