# Controlled Interacting Particle Algorithms for Simulation-based Reinforcement Learning

arXiv: https://arxiv.org/abs/2107.01244 · HTML: https://mlsanity.com/paper/2107.01244/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Jul 2021.

## Built on by (most cited first, 3 of 3)

- [Convex Q Learning in a Stochastic Environment: Extended Version](https://mlsanity.com/paper/2309.05105/) (2023)
- [Interacting Particle Systems for Fast Linear Quadratic RL](https://mlsanity.com/paper/2406.11057/) (2024)
- [Optimistic Training and Convergence of Q-Learning -- Extended Version](https://mlsanity.com/paper/2602.06146/) (2026)

## Abstract (arXiv)

This paper is concerned with optimal control problems for control systems in continuous time, and interacting particle system methods designed to construct approximate control solutions. Particular attention is given to the linear quadratic (LQ) control problem. There is a growing interest in re-visiting this classical problem, in part due to the successes of reinforcement learning (RL). The main question of this body of research (and also of our paper) is to approximate the optimal control law em without explicitly solving the Riccati equation. A novel simulation-based algorithm, namely a dual ensemble Kalman filter (EnKF), is introduced. The algorithm is used to obtain formulae for optimal control, expressed entirely in terms of the EnKF particles. An extension to the nonlinear case is also presented. The theoretical results and algorithms are illustrated with numerical experiments.