@inproceedings{
yao2023inverse,
title={Inverse Reinforcement Learning with Multiple Planning Horizons},
author={Jiayu Yao and Finale Doshi-Velez and Barbara Engelhardt},
booktitle={NeurIPS 2023 Workshop on Generalization in Planning},
year={2023},
url={https://openreview.net/forum?id=RaHA0fJMbL}
}
In this work, we study an inverse reinforcement learning (IRL) problem where the experts are planning \textit{under a shared reward function but with different, unknown planning horizons}. Without the knowledge of discount factors, the reward function has a larger feasible solution set, which makes it harder to identify a reward function. To overcome this challenge, we develop an algorithm that in practice, can learn a reward function similar to the true reward function. We give an empirical characterization of the identifiability and generalizability of the feasible set of the reward function.
Inverse Reinforcement Learning with Multiple Planning Horizons
Jiayu Yao Finale Doshi-Velez Barbara Engelhardt