Adaptable replanning with compressed linear action models for learning from demonstrations

Clement Gehring, Leslie Pack Kaelbling, Tomás Lozano-Pérez

Abstract: We propose an adaptable and efficient model-based reinforcement learning approach well suited for continuous domains with sparse samples, a setting often encountered when learning from demonstratio...