Victor Villin

Victor Villin, Thomas Kleine Buening, Christos Dimitrakakis

We propose a minimax-Bayes approach to Ad Hoc Teamwork (AHT) that optimizes policies against an adversarial prior over partners, explicitly accounting for uncertainty about partners at time of deployment. Unlike existing methods that assume a specific distribution over partners, our approach improves worst-case performance guarantees. Extensive experiments, including evaluations on coordinated cooking tasks from the Melting Pot suite, show our method's superior robustness compared to self-play, fictitious play, and best response learning. Our work highlights the importance of selecting an appropriate training distribution over teammates to achieve robustness in AHT.

Jakub Tłuczek, Victor Villin, Christos Dimitrakakis

Fairness is desirable yet challenging to achieve within multi-agent systems, especially when agents differ in latent traits that affect their abilities. This hidden heterogeneity often leads to unequal distributions of wealth, even when agents operate under the same rules. Motivated by real-world examples, we propose a framework based on repeated principal-agent games, where a principal, who also can be seen as a player of the game, learns to offer adaptive contracts to agents. By leveraging a simple yet powerful contract structure, we show that a fairness-aware principal can learn homogeneous linear contracts that equalize outcomes across agents in a sequential social dilemma. Importantly, this fairness does not come at the cost of efficiency: our results demonstrate that it is possible to promote equity and stability in the system while preserving overall performance.