# Controlling dynamics of stochastic systems with deep reinforcement learning

arXiv: https://arxiv.org/abs/2502.18111 · HTML: https://mlsanity.com/paper/2502.18111/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in RL theory and sample complexity, one of 15 directions in Deep reinforcement learning methods.
- Builds on 5 of its 16 references that are in the map.
- First posted on arXiv 25 Feb 2025.

## Placed in

- Deep reinforcement learning methods / [RL theory and sample complexity](https://mlsanity.com/deeprl/rl-theory-and-sample-complexity/)

## Builds on (most cited first, 5 of 5)

- [Q-learning with temporal memory to navigate turbulence](https://mlsanity.com/paper/2404.17495/) (2024)
- [An Efficient Deep Reinforcement Learning Model for Urban Traffic Control](https://mlsanity.com/paper/1808.01876/) (2018)
- [Emergence of Chemotactic Strategies with Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2404.01999/) (2024)
- [SwarmRL: Building the Future of Smart Active Systems](https://mlsanity.com/paper/2404.16388/) (2024)
- [SIR-RL: Reinforcement Learning for Optimized Policy Control during Epidemiological Outbreaks in Emerging Market and Developing Economies](https://mlsanity.com/paper/2404.08423/) (2024)

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

- [Predictive reinforcement learning based adaptive PID controller](https://mlsanity.com/paper/2506.08509/) (2025)

## Abstract (arXiv)

A properly designed controller can help improve the quality of experimental measurements or force a dynamical system to follow a completely new time-evolution path. Recent developments in deep reinforcement learning have made steep advances toward designing effective control schemes for fairly complex systems. However, a general simulation scheme that employs deep reinforcement learning for exerting control in stochastic systems is yet to be established. In this paper, we attempt to further bridge a gap between control theory and deep reinforcement learning by proposing a simulation algorithm that allows achieving control of the dynamics of stochastic systems through the use of trained artificial neural networks. Specifically, we use agent-based simulations where the neural network plays the role of the controller that drives local state-to-state transitions. We demonstrate the workflow and the effectiveness of the proposed control methods by considering the following two stochastic processes: particle coalescence on a lattice and a totally asymmetric exclusion process.