# Intrinsic motivations and open-ended learning

arXiv: https://arxiv.org/abs/1912.13263 · HTML: https://mlsanity.com/paper/1912.13263/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Dec 2019.

## Built on by (most cited first, 6 of 6)

- [An information-theoretic perspective on intrinsic motivation in reinforcement learning: a survey](https://mlsanity.com/paper/2209.08890/) (2022)
- [AI Autonomy : Self-Initiated Open-World Continual Learning and Adaptation](https://mlsanity.com/paper/2203.08994/) (2022)
- [Image-Based Deep Reinforcement Learning with Intrinsically Motivated Stimuli: On the Execution of Complex Robotic Tasks](https://mlsanity.com/paper/2407.21338/) (2024)
- [Self-supervised network distillation: an effective approach to exploration in sparse reward environments](https://mlsanity.com/paper/2302.11563/) (2023)
- [The Autonomy-Alignment Problem in Open-Ended Learning Robots: Formalising the Purpose Framework](https://mlsanity.com/paper/2403.02514/) (2024)
- [Towards a Formal Theory of the Need for Competence via Computational Intrinsic Motivation](https://mlsanity.com/paper/2502.07423/) (2025)

## Abstract (arXiv)

There is a growing interest and literature on intrinsic motivations and open-ended learning in both cognitive robotics and machine learning on one side, and in psychology and neuroscience on the other. This paper aims to review some relevant contributions from the two literature threads and to draw links between them. To this purpose, the paper starts by defining intrinsic motivations and by presenting a computationally-driven theoretical taxonomy of their different types. Then it presents relevant contributions from the psychological and neuroscientific literature related to intrinsic motivations, interpreting them based on the grid, and elucidates the mechanisms and functions they play in animals and humans. Endowed with such concepts and their biological underpinnings, the paper next presents a selection of models from cognitive robotics and machine learning that computationally operationalise the concepts of intrinsic motivations and links them to biology concepts. The contribution finally presents some of the open challenges of the field from both the psychological/neuroscientific and computational perspectives.