# Adaptive Variance for Changing Sparse-Reward Environments

arXiv: https://arxiv.org/abs/1903.06309 · HTML: https://mlsanity.com/paper/1903.06309/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Mar 2019.

## Built on by (most cited first, 2 of 2)

- [Goal-conditioned Imitation Learning](https://mlsanity.com/paper/1906.05838/) (2019)
- [Learning a Hierarchical Planner from Humans in Multiple Generations](https://mlsanity.com/paper/2310.11614/) (2023)

## Abstract (arXiv)

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.