Missing Bridges: Composition-Aware Active Imitation Learning
Active imitation learning reduces expert effort by allowing a learner to request the demonstrations it needs. Existing methods typically select these requests for their expected information gain about the expert policy. In structured multi-task domains, however, the number of start-goal tasks may grow combinatorially despite their solutions sharing reusable behavior. This makes composable behaviors especially valuable, since a single demonstration may help solve many tasks at once. Prior methods do not explicitly account for this value when selecting which demonstration to request. We introduce Adaptive Agents via Latent Topologies (AALT), which requests demonstrations that maximize expec...
| Authors | Maxwell J. Jacobson, Ahmed H Qureshi, Yexiang Xue |
|---|---|
| Primary category | cs.AI |
| Published | 16 September 2026 |
| Last updated at source | 16 September 2026 |
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