Do as the Romans Do: Learning Universal Behaviors from Heterogeneous Agents
Humans often acquire new skills by observing others, since observed behaviors implicitly reveal how to act reasonably in an environment. However, observations drawn from a heterogeneous population introduce conflicting behavioral signals, making it difficult to determine which behaviors are worth imitating. We address this challenge with General Reward Inference and Disentanglement (GRID), a social learning method that extracts universally useful behaviors from a heterogeneous population of demonstrators pursuing different goals. GRID decomposes per-agent reward functions into a general reward, capturing behaviors shared across all agents, and specific rewards, capturing individual prefer...
| Authors | Caleb Chang, Davin Win Kyi, Natasha Jaques et al. |
|---|---|
| Primary category | cs.LG |
| Published | 16 June 2026 |
| Last updated at source | 2 October 2026 |
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