# Model-Based Reinforcement Learning Control of Reaction-Diffusion Problems

arXiv: https://arxiv.org/abs/2402.14446 · HTML: https://mlsanity.com/paper/2402.14446/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in RL with diffusion and flow models, one of 15 directions in Deep reinforcement learning methods.
- Builds on 4 of its 30 references that are in the map.
- First posted on arXiv 22 Feb 2024.

## Placed in

- Deep reinforcement learning methods / [RL with diffusion and flow models](https://mlsanity.com/deeprl/rl-with-diffusion-and-flow-models/)

## Builds on (most cited first, 4 of 4)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [A Practical Guide to Multi-Objective Reinforcement Learning and Planning](https://mlsanity.com/paper/2103.09568/) (2021)
- [Optimising Lockdown Policies for Epidemic Control using Reinforcement Learning](https://mlsanity.com/paper/2003.14093/) (2020)
- [COVID-19 Pandemic Cyclic Lockdown Optimization Using Reinforcement Learning](https://mlsanity.com/paper/2009.04647/) (2020)

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

- [Operator-Theoretic Foundations and Policy Gradient Methods for General MDPs with Unbounded Costs](https://mlsanity.com/paper/2603.17875/) (2026)

## Abstract (arXiv)

Mathematical and computational tools have proven to be reliable in decision-making processes. In recent times, in particular, machine learning-based methods are becoming increasingly popular as advanced support tools. When dealing with control problems, reinforcement learning has been applied to decision-making in several applications, most notably in games. The success of these methods in finding solutions to complex problems motivates the exploration of new areas where they can be employed to overcome current difficulties. In this paper, we explore the use of automatic control strategies to initial boundary value problems in thermal and disease transport. Specifically, in this work, we adapt an existing reinforcement learning algorithm using a stochastic policy gradient method and we introduce two novel reward functions to drive the flow of the transported field. The new model-based framework exploits the interactions between a reaction-diffusion model and the modified agent. The results show that certain controls can be implemented successfully in these applications, although model simplifications had to be assumed.