Recently, offline RL algorithms have been proposed that remain adaptive at runtime. For example, the LION algorithm \cite{lion} provides the user with an interface to set the trade-off between behavior cloning and optimality w.r.t. the estimated return at runtime. Experts can then use this interface to adapt the policy behavior according to their preferences and find a good trade-off between conservatism and performance optimization. Since expert time is precious, we extend the methodology with an autopilot that automatically finds the correct parameterization of the trade-off, yielding a new algorithm which we term AutoLION.
Cite
@article{arxiv.2306.09744,
title = {Automatic Trade-off Adaptation in Offline RL},
author = {Phillip Swazinna and Steffen Udluft and Thomas Runkler},
journal= {arXiv preprint arXiv:2306.09744},
year = {2023}
}