On Overfitting and Asymptotic Bias in Batch Reinforcement Learning with Partial Observability (Extended Abstract)
Abstract
When an agent has limited information on its environment, the suboptimality of an RL algorithm can be decomposed into the sum of two terms: a term related to an asymptotic bias (suboptimality with unlimited data) and a term due to overfitting (additional suboptimality due to limited data). In the context of reinforcement learning with partial observability, this paper provides an analysis of the tradeoff between these two error sources. In particular, our theoretical analysis formally characterizes how a smaller state representation increases the asymptotic bias while decreasing the risk of overfitting.
Cite
Text
François-Lavet et al. "On Overfitting and Asymptotic Bias in Batch Reinforcement Learning with Partial Observability (Extended Abstract)." International Joint Conference on Artificial Intelligence, 2020. doi:10.24963/IJCAI.2020/706Markdown
[François-Lavet et al. "On Overfitting and Asymptotic Bias in Batch Reinforcement Learning with Partial Observability (Extended Abstract)." International Joint Conference on Artificial Intelligence, 2020.](https://mlanthology.org/ijcai/2020/francoislavet2020ijcai-overfitting/) doi:10.24963/IJCAI.2020/706BibTeX
@inproceedings{francoislavet2020ijcai-overfitting,
title = {{On Overfitting and Asymptotic Bias in Batch Reinforcement Learning with Partial Observability (Extended Abstract)}},
author = {François-Lavet, Vincent and Rabusseau, Guillaume and Pineau, Joelle and Ernst, Damien and Fonteneau, Raphael},
booktitle = {International Joint Conference on Artificial Intelligence},
year = {2020},
pages = {5055-5059},
doi = {10.24963/IJCAI.2020/706},
url = {https://mlanthology.org/ijcai/2020/francoislavet2020ijcai-overfitting/}
}