Improving Generalization in Meta Reinforcement Learning Using Learned Objectives
Abstract
Biological evolution has distilled the experiences of many learners into the general learning algorithms of humans. Our novel meta reinforcement learning algorithm MetaGenRL is inspired by this process. MetaGenRL distills the experiences of many complex agents to meta-learn a low-complexity neural objective function that decides how future individuals will learn. Unlike recent meta-RL algorithms, MetaGenRL can generalize to new environments that are entirely different from those used for meta-training. In some cases, it even outperforms human-engineered RL algorithms. MetaGenRL uses off-policy second-order gradients during meta-training that greatly increase its sample efficiency.
Cite
Text
Kirsch et al. "Improving Generalization in Meta Reinforcement Learning Using Learned Objectives." International Conference on Learning Representations, 2020.Markdown
[Kirsch et al. "Improving Generalization in Meta Reinforcement Learning Using Learned Objectives." International Conference on Learning Representations, 2020.](https://mlanthology.org/iclr/2020/kirsch2020iclr-improving/)BibTeX
@inproceedings{kirsch2020iclr-improving,
title = {{Improving Generalization in Meta Reinforcement Learning Using Learned Objectives}},
author = {Kirsch, Louis and van Steenkiste, Sjoerd and Schmidhuber, Jürgen},
booktitle = {International Conference on Learning Representations},
year = {2020},
url = {https://mlanthology.org/iclr/2020/kirsch2020iclr-improving/}
}