New research details sample efficiency of Inverse Dynamics Models in imitation learning

A new research paper explores the sample efficiency of Inverse Dynamics Models (IDMs) in semi-supervised imitation learning. The study demonstrates that VM-IDM and IDM labeling methods learn the same policy in a limiting case, termed the IDM-based policy. Researchers attribute the superior sample efficiency of IDM-based policies to their lower complexity hypothesis class and reduced stochasticity compared to expert policies, supported by statistical learning theory and experiments on benchmarks like Procgen and LIBERO. The paper also introduces an improved LAPO algorithm for latent action policy learning. AI

IMPACT Provides theoretical insights into sample efficiency for imitation learning, potentially improving agent performance in complex environments.

RANK_REASON The cluster contains a research paper published on arXiv detailing theoretical and experimental findings in machine learning. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

• arXiv
• imitation learning
• Inverse Dynamics Models
• Lapito
• Libero
• Procgen
• Sacha Morin
• Semi-Supervised Imitation Learning
• Unified Video-Action Prediction
• Video modeling

• paper
• model release

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