# Diverse Exploration for Fast and Safe Policy Improvement

arXiv: https://arxiv.org/abs/1802.08331 · HTML: https://mlsanity.com/paper/1802.08331/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Feb 2018.

## Built on by (most cited first, 1 of 1)

- [Learning Quadruped Locomotion Policies using Logical Rules](https://mlsanity.com/paper/2107.10969/) (2021)

## Abstract (arXiv)

We study an important yet under-addressed problem of quickly and safely improving policies in online reinforcement learning domains. As its solution, we propose a novel exploration strategy - diverse exploration (DE), which learns and deploys a diverse set of safe policies to explore the environment. We provide DE theory explaining why diversity in behavior policies enables effective exploration without sacrificing exploitation. Our empirical study shows that an online policy improvement algorithm framework implementing the DE strategy can achieve both fast policy improvement and safe online performance.