Events as Triggers for Behavioral Diversity in Multi-Agent Reinforcement Learning
Effective multi-agent cooperation requires agents to adopt diverse behaviors as task conditions evolve-and to do so at the right moment. Yet, current Multi-Agent Reinforcement Learning (MARL) frameworks that facilitate this diversity are still limited by the fact that they bind fixed behaviors to fixed agent identities. Consequently, they are ill-equipped for tasks where agents need to take on different roles at very specific moments in time. We argue that, to define these behavioral transitions, the missing ingredient is $\textbf{events}$. Events are changes in the state of the system that induce qualitative changes in the task. Based on this view, we introduce a framework that decouples...
| Authors | Hannes Büchi, Manon Flageat, Eduardo Sebastián et al. |
|---|---|
| Primary category | cs.MA |
| Published | 12 May 2026 |
| Last updated at source | 13 May 2026 |
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