# World Discovery Models

arXiv: https://arxiv.org/abs/1902.07685 · HTML: https://mlsanity.com/paper/1902.07685/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Feb 2019.

## Built on by (most cited first, 10 of 10)

- [BYOL-Explore: Exploration by Bootstrapped Prediction](https://mlsanity.com/paper/2206.08332/) (2022)
- [An information-theoretic perspective on intrinsic motivation in reinforcement learning: a survey](https://mlsanity.com/paper/2209.08890/) (2022)
- [Information is Power: Intrinsic Control via Information Capture](https://mlsanity.com/paper/2112.03899/) (2021)
- [Curiosity in Hindsight: Intrinsic Exploration in Stochastic Environments](https://mlsanity.com/paper/2211.10515/) (2022)
- [An Empirical Study on the Power of Future Prediction in Partially Observable Environments](https://mlsanity.com/paper/2402.07102/) (2024)
- [Representations and Exploration for Deep Reinforcement Learning using Singular Value Decomposition](https://mlsanity.com/paper/2305.00654/) (2023)
- [What You Think is What You See: Driving Exploration in VLM Agents via Visual-Linguistic Curiosity](https://mlsanity.com/paper/2605.03782/) (2026)
- [ObserveNet Control: A Vision-Dynamics Learning Approach to Predictive Control in Autonomous Vehicles](https://mlsanity.com/paper/2107.08690/) (2021)
- [Generative Intrinsic Optimization: Intrinsic Control with Model Learning](https://mlsanity.com/paper/2310.08100/) (2023)
- [Can In-Context Learning Support Intrinsic Curiosity?](https://mlsanity.com/paper/2606.19476/) (2026)

## Abstract (arXiv)

As humans we are driven by a strong desire for seeking novelty in our world. Also upon observing a novel pattern we are capable of refining our understanding of the world based on the new information---humans can discover their world. The outstanding ability of the human mind for discovery has led to many breakthroughs in science, art and technology. Here we investigate the possibility of building an agent capable of discovering its world using the modern AI technology. In particular we introduce NDIGO, Neural Differential Information Gain Optimisation, a self-supervised discovery model that aims at seeking new information to construct a global view of its world from partial and noisy observations. Our experiments on some controlled 2-D navigation tasks show that NDIGO outperforms state-of-the-art information-seeking methods in terms of the quality of the learned representation. The improvement in performance is particularly significant in the presence of white or structured noise where other information-seeking methods follow the noise instead of discovering their world.