# Taming Lagrangian Chaos with Multi-Objective Reinforcement Learning

arXiv: https://arxiv.org/abs/2212.09612 · HTML: https://mlsanity.com/paper/2212.09612/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 38 references that are in the map.
- First posted on arXiv 19 Dec 2022.

## Builds on (most cited first, 6 of 6)

- [Efficient collective swimming by harnessing vortices through deep reinforcement learning](https://mlsanity.com/paper/1802.02674/) (2018)
- [Zermelo's problem: Optimal point-to-point navigation in 2D turbulent flows using Reinforcement Learning](https://mlsanity.com/paper/1907.08591/) (2019)
- [Machine learning strategies for path-planning microswimmers in turbulent flows](https://mlsanity.com/paper/1910.01728/) (2019)
- [Reinforcement learning for pursuit and evasion of microswimmers at low Reynolds number](https://mlsanity.com/paper/2106.08609/) (2021)
- [Optimal control of point-to-point navigation in turbulent time-dependent flows using Reinforcement Learning](https://mlsanity.com/paper/2103.00329/) (2021)
- [Optimizing Airborne Wind Energy with Reinforcement Learning](https://mlsanity.com/paper/2203.14271/) (2022)

## Built on by (most cited first, 2 of 2)

- [Physics-Guided Actor-Critic Reinforcement Learning for Swimming in Turbulence](https://mlsanity.com/paper/2406.10242/) (2024)
- [A critical assessment of reinforcement learning methods for microswimmer navigation in complex flows](https://mlsanity.com/paper/2505.05525/) (2025)

## Abstract (arXiv)

We consider the problem of two active particles in 2D complex flows with the multi-objective goals of minimizing both the dispersion rate and the energy consumption of the pair. We approach the problem by means of Multi Objective Reinforcement Learning (MORL), combining scalarization techniques together with a Q-learning algorithm, for Lagrangian drifters that have variable swimming velocity. We show that MORL is able to find a set of trade-off solutions forming an optimal Pareto frontier. As a benchmark, we show that a set of heuristic strategies are dominated by the MORL solutions. We consider the situation in which the agents cannot update their control variables continuously, but only after a discrete (decision) time, τ. We show that there is a range of decision times, in between the Lyapunov time and the continuous updating limit, where Reinforcement Learning finds strategies that significantly improve over heuristics. In particular, we discuss how large decision times require enhanced knowledge of the flow, whereas for smaller τ all a priori heuristic strategies become Pareto optimal.