# Empowerment -- an Introduction

arXiv: https://arxiv.org/abs/1310.1863 · HTML: https://mlsanity.com/paper/1310.1863/

## Key facts

- Cited by 68 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Oct 2013.

## Built on by (most cited first, 30 of 68)

- [Reward learning from human preferences and demonstrations in Atari](https://mlsanity.com/paper/1811.06521/) (2018)
- [Causal Influence Detection for Improving Efficiency in Reinforcement Learning](https://mlsanity.com/paper/2106.03443/) (2021)
- [Exploration in Deep Reinforcement Learning: From Single-Agent to Multiagent Domain](https://mlsanity.com/paper/2109.06668/) (2021)
- [Towards Robust Bisimulation Metric Learning](https://mlsanity.com/paper/2110.14096/) (2021)
- [MADE: Exploration via Maximizing Deviation from Explored Regions](https://mlsanity.com/paper/2106.10268/) (2021)
- [The Information Geometry of Unsupervised Reinforcement Learning](https://mlsanity.com/paper/2110.02719/) (2021)
- [Adversarial Imitation via Variational Inverse Reinforcement Learning](https://mlsanity.com/paper/1809.06404/) (2018)
- [MaxInfoRL: Boosting exploration in reinforcement learning through information gain maximization](https://mlsanity.com/paper/2412.12098/) (2024)
- [Characterizing AI Agents for Alignment and Governance](https://mlsanity.com/paper/2504.21848/) (2025)
- [An information-theoretic perspective on intrinsic motivation in reinforcement learning: a survey](https://mlsanity.com/paper/2209.08890/) (2022)
- [Beyond Fine-Tuning: Transferring Behavior in Reinforcement Learning](https://mlsanity.com/paper/2102.13515/) (2021)
- [Mutual Information State Intrinsic Control](https://mlsanity.com/paper/2103.08107/) (2021)
- [Learn Goal-Conditioned Policy with Intrinsic Motivation for Deep Reinforcement Learning](https://mlsanity.com/paper/2104.05043/) (2021)
- [Active Hierarchical Exploration with Stable Subgoal Representation Learning](https://mlsanity.com/paper/2105.14750/) (2021)
- [Understanding the World Through Action](https://mlsanity.com/paper/2110.12543/) (2021)
- [Variational Empowerment as Representation Learning for Goal-Based Reinforcement Learning](https://mlsanity.com/paper/2106.01404/) (2021)
- [Learning to Assist Humans without Inferring Rewards](https://mlsanity.com/paper/2411.02623/) (2024)
- [Braxlines: Fast and Interactive Toolkit for RL-driven Behavior Engineering beyond Reward Maximization](https://mlsanity.com/paper/2110.04686/) (2021)
- [CLUE: Calibrated Latent Guidance for Offline Reinforcement Learning](https://mlsanity.com/paper/2306.13412/) (2023)
- [Variational Curriculum Reinforcement Learning for Unsupervised Discovery of Skills](https://mlsanity.com/paper/2310.19424/) (2023)
- [Intrinsic Motivation in Dynamical Control Systems](https://mlsanity.com/paper/2301.00005/) (2022)
- [Intrinsically-Motivated Humans and Agents in Open-World Exploration](https://mlsanity.com/paper/2503.23631/) (2025)
- [Hierarchical Empowerment: Towards Tractable Empowerment-Based Skill Learning](https://mlsanity.com/paper/2307.02728/) (2023)
- [SOMBRL: Scalable and Optimistic Model-Based RL](https://mlsanity.com/paper/2511.20066/) (2025)
- [Explore and Control with Adversarial Surprise](https://mlsanity.com/paper/2107.07394/) (2021)
- [Leveraging Jumpy Models for Planning and Fast Learning in Robotic Domains](https://mlsanity.com/paper/2302.12617/) (2023)
- [Controlled Diversity with Preference : Towards Learning a Diverse Set of Desired Skills](https://mlsanity.com/paper/2303.04592/) (2023)
- [Constrained Skill Discovery: Quadruped Locomotion with Unsupervised Reinforcement Learning](https://mlsanity.com/paper/2410.07877/) (2024)
- [Towards Empowerment Gain through Causal Structure Learning in Model-Based RL](https://mlsanity.com/paper/2502.10077/) (2025)
- [From Curiosity to Competence: How World Models Interact with the Dynamics of Exploration](https://mlsanity.com/paper/2507.08210/) (2025)

## Abstract (arXiv)

This book chapter is an introduction to and an overview of the information-theoretic, task independent utility function "Empowerment", which is defined as the channel capacity between an agent's actions and an agent's sensors. It quantifies how much influence and control an agent has over the world it can perceive. This book chapter discusses the general idea behind empowerment as an intrinsic motivation and showcases several previous applications of empowerment to demonstrate how empowerment can be applied to different sensor-motor configuration, and how the same formalism can lead to different observed behaviors. Furthermore, we also present a fast approximation for empowerment in the continuous domain.