# Autonomous exploration for navigating in non-stationary CMPs

arXiv: https://arxiv.org/abs/1910.08446 · HTML: https://mlsanity.com/paper/1910.08446/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Oct 2019.

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

- [Geometric Entropic Exploration](https://mlsanity.com/paper/2101.02055/) (2021)
- [Action Redundancy in Reinforcement Learning](https://mlsanity.com/paper/2102.11329/) (2021)

## Abstract (arXiv)

We consider a setting in which the objective is to learn to navigate in a controlled Markov process (CMP) where transition probabilities may abruptly change. For this setting, we propose a performance measure called exploration steps which counts the time steps at which the learner lacks sufficient knowledge to navigate its environment efficiently. We devise a learning meta-algorithm, MNM and prove an upper bound on the exploration steps in terms of the number of changes.