# Curiosity-driven reinforcement learning with homeostatic regulation

arXiv: https://arxiv.org/abs/1801.07440 · HTML: https://mlsanity.com/paper/1801.07440/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Jan 2018.

## Built on by (most cited first, 4 of 4)

- [Exploration in Deep Reinforcement Learning: A Survey](https://mlsanity.com/paper/2205.00824/) (2022)
- [Discovering Intrinsic Reward with Contrastive Random Walk](https://mlsanity.com/paper/2204.10976/) (2022)
- [Dynamic Memory-based Curiosity: A Bootstrap Approach for Exploration](https://mlsanity.com/paper/2208.11349/) (2022)
- [CrowdTransfer: Enabling Crowd Knowledge Transfer in AIoT Community](https://mlsanity.com/paper/2407.06485/) (2024)

## Abstract (arXiv)

We propose a curiosity reward based on information theory principles and consistent with the animal instinct to maintain certain critical parameters within a bounded range. Our experimental validation shows the added value of the additional homeostatic drive to enhance the overall information gain of a reinforcement learning agent interacting with a complex environment using continuous actions. Our method builds upon two ideas: i) To take advantage of a new Bellman-like equation of information gain and ii) to simplify the computation of the local rewards by avoiding the approximation of complex distributions over continuous states and actions.