# Learning to flock through reinforcement

arXiv: https://arxiv.org/abs/1911.01697 · HTML: https://mlsanity.com/paper/1911.01697/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Nov 2019.

## Built on by (most cited first, 4 of 4)

- [Reinforcement Learning for Active Matter](https://mlsanity.com/paper/2503.23308/) (2025)
- [High Performance Simulation for Scalable Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2207.03945/) (2022)
- [Unveiling Complex Collective Behaviors from Simple Rewards](https://mlsanity.com/paper/2607.12861/) (2026)
- [Emergent aggregation from collective foraging](https://mlsanity.com/paper/2608.28046/) (2026)

## Abstract (arXiv)

Flocks of birds, schools of fish, insects swarms are examples of coordinated motion of a group that arises spontaneously from the action of many individuals. Here, we study flocking behavior from the viewpoint of multi-agent reinforcement learning. In this setting, a learning agent tries to keep contact with the group using as sensory input the velocity of its neighbors. This goal is pursued by each learning individual by exerting a limited control on its own direction of motion. By means of standard reinforcement learning algorithms we show that: i) a learning agent exposed to a group of teachers, i.e. hard-wired flocking agents, learns to follow them, and ii) that in the absence of teachers, a group of independently learning agents evolves towards a state where each agent knows how to flock. In both scenarios, i) and ii), the emergent policy (or navigation strategy) corresponds to the polar velocity alignment mechanism of the well-known Vicsek model. These results show that a) such a velocity alignment may have naturally evolved as an adaptive behavior that aims at minimizing the rate of neighbor loss, and b) prove that this alignment does not only favor (local) polar order, but it corresponds to best policy/strategy to keep group cohesion when the sensory input is limited to the velocity of neighboring agents. In short, to stay together, steer together.