Optimizing Team Behavior via Extremum-seeking Control in Multi-agent Reinforcement Learning
In cooperative Multi-Agent Reinforcement Learning (MARL), agents must learn to coordinate their actions to achieve shared objectives. A key indicator of effective coordination is behavioral diversity, i.e., the agents' potential to enact distinct yet complementary control policies given the same set of observations. Although behavioral diversity can enable role specialization and richer joint behaviors, its appropriate level is highly context- and task-dependent and often costly to tune. We propose Adaptive Diversity Control (ADiCo), a two-timescale framework that adapts behavioral diversity online during training. On a fast timescale, Diversity Control (DiCo) regulates system diversity t...
| Authors | Svar Rajankumar Patel, Kshitij Jerath |
|---|---|
| Primary category | cs.MA |
| Published | 13 October 2026 |
| Last updated at source | 13 October 2026 |
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