2 papers
cs.LG2026
Noise-Guided Transport for Imitation Learning
Lionel Blondé, Joao A. Candido Ramos, Alexandros Kalousis
We consider imitation learning in the low-data regime, where only a limited number of expert demonstrations are available. In this setting, methods that rely on large-scale pretrai…
cs.LG2024
Mimicking Better by Matching the Approximate Action Distribution
João A. Cândido Ramos, Lionel Blondé, Naoya Takeishi +1
In this paper, we introduce MAAD, a novel, sample-efficient on-policy algorithm for Imitation Learning from Observations. MAAD utilizes a surrogate reward signal, which can be deri…