PopGen: Generating Semantically Diverse Populations for Zero-Shot Coordination
In multi-agent reinforcement learning, exposing a cooperator to partners with diverse strategies during training is key to better collaboration at test time. However, existing methods typically rely on trajectory-level proxies to measure diversity, which may label two distinct trajectories of the same strategy as diverse. Instead, our key insight is to leverage LLMs' ability to describe behaviorally distinct agents in environments, similar to how humans can recognize high-level strategies. To this end, we introduce PopGen, a framework that synthesizes semantically diverse agents from scratch using LLMs. PopGen first generates a diverse set of textual descriptions of agent behaviors, then ...
| Authors | Paul Teiletche, Kanghyun Ryu, Kyungjoon Park et al. |
|---|---|
| Primary category | cs.AI |
| Published | 11 October 2026 |
| Last updated at source | 11 October 2026 |
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