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Related Experiment Video

Updated: Jun 21, 2026

Quantifying Learning in Young Infants: Tracking Leg Actions During a Discovery-learning Task

Quantifying Learning in Young Infants: Tracking Leg Actions During a Discovery-learning Task

Published on: June 1, 2015

Reinforcement learning or active inference?

Karl J Friston¹, Jean Daunizeau, Stefan J Kiebel

¹The Wellcome Trust Centre for Neuroimaging, University College London, London, United Kingdom. k.friston@fil.ion.ucl.ac.uk

|July 31, 2009

Summary

This summary is machine-generated.

This study demonstrates that a free-energy principle can optimize agent behavior, eliminating the need for reinforcement learning or control theory. This approach unifies action and perception, offering a new perspective on adaptive behavior and brain function.

Related Experiment Videos

Last Updated: Jun 21, 2026

Quantifying Learning in Young Infants: Tracking Leg Actions During a Discovery-learning Task

Quantifying Learning in Young Infants: Tracking Leg Actions During a Discovery-learning Task

Published on: June 1, 2015

Area of Science:

Computational Neuroscience
Artificial Intelligence
Cognitive Science

Background:

Traditional approaches to optimizing agent behavior often rely on reinforcement learning or control theory.
These methods typically involve concepts like reward, value, or utility.

Purpose of the Study:

To investigate if a free-energy formulation of perception can optimize complex and adaptive behaviors.
To demonstrate that this approach can reproduce policies optimized by traditional methods without invoking reward or utility.

Main Methods:

Utilizing a free-energy principle where agents minimize free-energy by adjusting internal states and environmental sampling.
Applying active perception and inference under the free-energy principle to solve the benchmark mountain-car problem.

Main Results:

Agents successfully learned complex, adaptive behaviors through self-supervised learning of causal environmental structures.
Behavioral policies generated matched those optimized by reinforcement learning and dynamic programming.
The notion of reward, value, or utility was not required for optimization.

Conclusions:

The free-energy formulation provides a unified account of action and perception.
This approach offers a potential alternative to reinforcement learning for behavior optimization.
Findings may prompt a reappraisal of dopamine's role in the brain.