@inproceedings{
karia2023epistemic,
title={Epistemic Exploration for Generalizable Planning and Learning in Non-Stationary Stochastic Settings},
author={Rushang Karia and Pulkit Verma and Gaurav Vipat and Siddharth Srivastava},
booktitle={NeurIPS 2023 Workshop on Generalization in Planning},
year={2023},
url={https://openreview.net/forum?id=bFPbN1nWgU}
}
Reinforcement Learning (RL) provides a convenient framework for sequential decision making when closed-form transition dynamics are unavailable and can frequently change. However, the high sample complexity of RL approaches limits their utility in the real-world. This paper presents an approach that performs meta-level exploration in the space of models and uses the learned models to compute policies. Our approach interleaves learning and planning allowing data-efficient, task-focused sample collection in the presence of non-stationarity. We conduct an empirical evaluation on benchmark domains and show that our approach significantly outperforms baselines in sample complexity and easily adapts to changing transition systems across tasks.
Epistemic Exploration for Generalizable Planning and Learning in Non-Stationary Stochastic Settings
Rushang Karia Pulkit Verma Gaurav Vipat Siddharth Srivastava