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Asking Easy Questions: A User-Friendly Approach to Active Reward Learning

2019 · arXiv (Cornell University)

Robots can learn the right reward function by querying a human expert. Existing approaches attempt to choose questions where the robot is most uncertain about the human's response; however, they do not consider how easy …

Learning Preferences for Interactive Autonomy

2022 · arXiv (Cornell University)

When robots enter everyday human environments, they need to understand their tasks and how they should perform those tasks. To encode these, reward functions, which specify the objective of a robot, are employed. However, designing …

Multi-Agent Inverse Q-Learning from Demonstrations

2025

When reward functions are hand-designed, deep reinforcement learning algorithms often suffer from reward misspecification, causing them to learn suboptimal policies in terms of the intended task objectives. In the single-agent case, inverse reinforcement learning (IRL) …