# A Reinforcement Learning Approach for Transient Control of Liquid Rocket Engines

arXiv: https://arxiv.org/abs/2006.11108 · HTML: https://mlsanity.com/paper/2006.11108/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Jun 2020.

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

- [Machine Learning Methods for the Design and Operation of Liquid Rocket Engines -- Research Activities at the DLR Institute of Space Propulsion](https://mlsanity.com/paper/2102.07109/) (2021)
- [Revisiting Space Mission Planning: A Reinforcement Learning-Guided Approach for Multi-Debris Rendezvous](https://mlsanity.com/paper/2409.16882/) (2024)
- [Realizing Stabilized Landing for Computation-Limited Reusable Rockets: A Quantum Reinforcement Learning Approach](https://mlsanity.com/paper/2310.06541/) (2023)
- [Timescale Separation Enables Deep Reinforcement Learning Control of Rotating Detonation Engine Mode Transitions](https://mlsanity.com/paper/2604.14398/) (2026)

## Abstract (arXiv)

Nowadays, liquid rocket engines use closed-loop control at most near steady operating conditions. The control of the transient phases is traditionally performed in open-loop due to highly nonlinear system dynamics. This situation is unsatisfactory, in particular for reusable engines. The open-loop control system cannot provide optimal engine performance due to external disturbances or the degeneration of engine components over time. In this paper, we study a deep reinforcement learning approach for optimal control of a generic gas-generator engine's continuous start-up phase. It is shown that the learned policy can reach different steady-state operating points and convincingly adapt to changing system parameters. A quantitative comparison with carefully tuned open-loop sequences and PID controllers is included. The deep reinforcement learning controller achieves the highest performance and requires only minimal computational effort to calculate the control action, which is a big advantage over approaches that require online optimization, such as model predictive control. control.