Adaptive Variance for Changing Sparse-Reward Environments

Xingyu Lin; Pengsheng Guo; Carlos Florensa; David Held

ICRA 2019poster7 citations

Adaptive Variance for Changing Sparse-Reward Environments

Xingyu Lin, Pengsheng Guo, Carlos Florensa, David Held

Abstract

Robots that are trained to perform a task in a fixed environment often fail when facing unexpected changes to the environment due to a lack of exploration. We propose a principled way to adapt the policy for better exploration in changing sparse-reward environments. Unlike previous works which explicitly model environmental changes, we analyze the relationship between the value function and the optimal exploration for a Gaussian-parameterized policy and show that our theory leads to an effective strategy for adjusting the variance of the policy, enabling fast adapt to changes in a variety of sparse-reward environments.

BibTeX

@inproceedings{icra2019_adaptivevariance,
  title = {Adaptive Variance for Changing Sparse-Reward Environments},
  author = {Xingyu Lin and Pengsheng Guo and Carlos Florensa and David Held},
  booktitle = {ICRA 2019},
  year = {2019}
}