# Decentralized Traffic Flow Optimization Through Intrinsic Motivation

arXiv: https://arxiv.org/abs/2505.11520 · HTML: https://mlsanity.com/paper/2505.11520/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 40 references that are in the map.
- First posted on arXiv 8 May 2025.

## Builds on (most cited first, 10 of 10)

- [Dynamics-Aware Unsupervised Discovery of Skills](https://mlsanity.com/paper/1907.01657/) (2019)
- [Hierarchical Deep Reinforcement Learning: Integrating Temporal Abstraction and Intrinsic Motivation](https://mlsanity.com/paper/1604.06057/) (2016)
- [Variational Intrinsic Control](https://mlsanity.com/paper/1611.07507/) (2016)
- [AvE: Assistance via Empowerment](https://mlsanity.com/paper/2006.14796/) (2020)
- [Scalable Multiagent Driving Policies For Reducing Traffic Congestion](https://mlsanity.com/paper/2103.00058/) (2021)
- [Changing the Environment Based on Empowerment as Intrinsic Motivation](https://mlsanity.com/paper/1406.1767/) (2014)
- [Efficient Empowerment Estimation for Unsupervised Stabilization](https://mlsanity.com/paper/2007.07356/) (2020)
- [Intrinsic Motivation in Dynamical Control Systems](https://mlsanity.com/paper/2301.00005/) (2022)
- [Intrinsically motivated collective motion](https://mlsanity.com/paper/1907.08097/) (2019)
- [Learning a Robust Multiagent Driving Policy for Traffic Congestion Reduction](https://mlsanity.com/paper/2112.03759/) (2021)

## Built on by (most cited first, 1 of 1)

- [Emergence of Physical Intelligence via Controllable Information Production](https://mlsanity.com/paper/2601.22449/) (2026)

## Abstract (arXiv)

Traffic congestion has long been an ubiquitous problem that is exacerbating with the rapid growth of megacities. In this proof-of-concept work we study intrinsic motivation, implemented via the empowerment principle, to control autonomous car behavior to improve traffic flow. In standard models of traffic dynamics, self-organized traffic jams emerge spontaneously from the individual behavior of cars, affecting traffic over long distances. Our novel car behavior strategy improves traffic flow while still being decentralized and using only locally available information without explicit coordination. Decentralization is essential for various reasons, not least to be able to absorb robustly substantial levels of uncertainty. Our scenario is based on the well-established traffic dynamics model, the Nagel-Schreckenberg cellular automaton. In a fraction of the cars in this model, we substitute the default behavior by empowerment, our intrinsic motivation-based method. This proposed model significantly improves overall traffic flow, mitigates congestion, and reduces the average traffic jam time.