Trustworthy Cooperation in Heterogeneous Teams

Multi-agent problems in which each agent only has a partial view of the environment are notoriously difficult, as agents must update stochastic beliefs on the knowledge and actions of other agents. Yet, communication can enable highly effective and scalable decentralised policies, by overcoming partial observability and enabling better cooperation.

Recent work has shown the potential benefits of behavioural heterogeneity in multi-agent systems, including resilience to observation noise. Motivated by this, we aim to provide new theoretical foundations, algorithms and optimization frameworks that facilitate controlled behavioural heterogeneity in agent teams.

We will jointly tackle the multi-agent learning and communication problems over noisy, resource-limited communication links, leading to a new class of problems.