VariBAD: A Very Good Method for Bayes-Adaptive Deep RL via Meta-Learning
Abstract
Trading off exploration and exploitation in an unknown environment is key to maximising expected return during learning. A Bayes-optimal policy, which does so optimally, conditions its actions not only on the environment state but on the agent's uncertainty about the environment. Computing a Bayes-optimal policy is however intractable for all but the smallest tasks. In this paper, we introduce variational Bayes-Adaptive Deep RL (variBAD), a way to meta-learn to perform approximate inference in an unknown environment, and incorporate task uncertainty directly during action selection. In a grid-world domain, we illustrate how variBAD performs structured online exploration as a function of task uncertainty. We further evaluate variBAD on MuJoCo domains widely used in meta-RL and show that it achieves higher online return than existing methods.
Cite
@article{arxiv.1910.08348,
title = {VariBAD: A Very Good Method for Bayes-Adaptive Deep RL via Meta-Learning},
author = {Luisa Zintgraf and Kyriacos Shiarlis and Maximilian Igl and Sebastian Schulze and Yarin Gal and Katja Hofmann and Shimon Whiteson},
journal= {arXiv preprint arXiv:1910.08348},
year = {2020}
}
Comments
Published at ICLR 2020