Learning Reward Functions by Integrating Human Demonstrations and Preferences

21 Jun 2019Malayandi PalanNicholas C. LandolfiGleb ShevchukDorsa Sadigh

Our goal is to accurately and efficiently learn reward functions for autonomous robots. Current approaches to this problem include inverse reinforcement learning (IRL), which uses expert demonstrations, and preference-based learning, which iteratively queries the user for her preferences between trajectories... (read more)

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