# Computational Theories of Curiosity-Driven Learning

arXiv: https://arxiv.org/abs/1802.10546 · HTML: https://mlsanity.com/paper/1802.10546/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Placed in Latent dynamics models and model-based RL, one of 11 directions in World models.
- Builds on 4 of its 72 references that are in the map.
- First posted on arXiv 28 Feb 2018.

## Placed in

- World models / [Latent dynamics models and model-based RL](https://mlsanity.com/wm/latent-dynamics-models-and-model-based-rl/)

## Builds on (most cited first, 4 of 4)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Continuous control with deep reinforcement learning](https://mlsanity.com/paper/1509.02971/) (2015)
- [Reinforcement Learning with Unsupervised Auxiliary Tasks](https://mlsanity.com/paper/1611.05397/) (2016)
- [Intrinsically Motivated Goal Exploration Processes with Automatic Curriculum Learning](https://mlsanity.com/paper/1708.02190/) (2017)

## Built on by (most cited first, 11 of 11)

- [Intelligent problem-solving as integrated hierarchical reinforcement learning](https://mlsanity.com/paper/2208.08731/) (2022)
- [Backprop-Free Reinforcement Learning with Active Neural Generative Coding](https://mlsanity.com/paper/2107.07046/) (2021)
- [Reinforcement Learning via Implicit Imitation Guidance](https://mlsanity.com/paper/2506.07505/) (2025)
- [Don't Do What Doesn't Matter: Intrinsic Motivation with Action Usefulness](https://mlsanity.com/paper/2105.09992/) (2021)
- [CubeTR: Learning to Solve The Rubiks Cube Using Transformers](https://mlsanity.com/paper/2111.06036/) (2021)
- [Using Curiosity for an Even Representation of Tasks in Continual Offline Reinforcement Learning](https://mlsanity.com/paper/2312.03177/) (2023)
- [Speculative Exploration on the Concept of Artificial Agents Conducting Autonomous Research](https://mlsanity.com/paper/2312.03497/) (2023)
- [VCAT: Vulnerability-aware and Curiosity-driven Adversarial Training for Enhancing Autonomous Vehicle Robustness](https://mlsanity.com/paper/2409.12997/) (2024)
- [Wind Estimation in Unmanned Aerial Vehicles with Causal Machine Learning](https://mlsanity.com/paper/2407.01154/) (2024)
- [Multi-scale structural complexity as a quantitative measure of visual complexity](https://mlsanity.com/paper/2408.04076/) (2024)
- [Parameter Estimation using Reinforcement Learning Causal Curiosity: Limits and Challenges](https://mlsanity.com/paper/2505.08453/) (2025)

## Abstract (arXiv)

What are the functions of curiosity? What are the mechanisms of curiosity-driven learning? We approach these questions about the living using concepts and tools from machine learning and developmental robotics. We argue that curiosity-driven learning enables organisms to make discoveries to solve complex problems with rare or deceptive rewards. By fostering exploration and discovery of a diversity of behavioural skills, and ignoring these rewards, curiosity can be efficient to bootstrap learning when there is no information, or deceptive information, about local improvement towards these problems. We also explain the key role of curiosity for efficient learning of world models. We review both normative and heuristic computational frameworks used to understand the mechanisms of curiosity in humans, conceptualizing the child as a sense-making organism. These frameworks enable us to discuss the bi-directional causal links between curiosity and learning, and to provide new hypotheses about the fundamental role of curiosity in self-organizing developmental structures through curriculum learning. We present various developmental robotics experiments that study these mechanisms in action, both supporting these hypotheses to understand better curiosity in humans and opening new research avenues in machine learning and artificial intelligence. Finally, we discuss challenges for the design of experimental paradigms for studying curiosity in psychology and cognitive neuroscience. Keywords: Curiosity, intrinsic motivation, lifelong learning, predictions, world model, rewards, free-energy principle, learning progress, machine learning, AI, developmental robotics, development, curriculum learning, self-organization.