# Learning controllable dynamics through informative exploration

arXiv: https://arxiv.org/abs/2507.06582 · HTML: https://mlsanity.com/paper/2507.06582/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 13 references that are in the map.
- First posted on arXiv 9 Jul 2025.

## Builds on (most cited first, 1 of 1)

- [A Dynamic Programming Algorithm for Finding an Optimal Sequence of Informative Measurements](https://mlsanity.com/paper/2109.11808/) (2021)

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

- [An Optimal Policy for Learning Controllable Dynamics by Exploration](https://mlsanity.com/paper/2512.20053/) (2025)

## Abstract (arXiv)

Environments with controllable dynamics are usually understood in terms of explicit models. However, such models are not always available, but may sometimes be learned by exploring an environment. In this work, we investigate using an information measure called "predicted information gain" to determine the most informative regions of an environment to explore next. Applying methods from reinforcement learning allows good suboptimal exploring policies to be found, and leads to reliable estimates of the underlying controllable dynamics. This approach is demonstrated by comparing with several myopic exploration approaches.