mlsanityData to 14 Oct 2026

Python-Based Reinforcement Learning on Simulink Models

arXiv 2405.08567 ↗First posted 14 May 2024cs.LG cs.SY eess.SYcited by 9

On the map: Deep reinforcement learning methods → Deep RL algorithms and representations

Abstract (arXiv)

This paper proposes a framework for training Reinforcement Learning agents using Python in conjunction with Simulink models. Leveraging Python's superior customization options and popular libraries like Stable Baselines3, we aim to bridge the gap between the established Simulink environment and the flexibility of Python for training bleeding edge agents. Our approach is demonstrated on the Quanser Aero 2, a versatile dual-rotor helicopter. We show that policies trained on Simulink models can be seamlessly transferred to the real system, enabling efficient development and deployment of Reinforcement Learning agents for control tasks. Through systematic integration steps, including C-code generation from Simulink, DLL compilation, and Python interface development, we establish a robust framework for training agents on Simulink models. Experimental results demonstrate the effectiveness of our approach, surpassing previous efforts and highlighting the potential of combining Simulink with Python for Reinforcement Learning research and applications.

Key facts. Cited by 9 papers in the map (data through 14 Oct 2026). Placed in Deep RL algorithms and representations, one of 15 directions in Deep reinforcement learning methods. Builds on 3 of its 12 references that are in the map. First posted on arXiv 14 May 2024.

Builds on

3 of 12 references.

References · 3

  1. 2017Deep Reinforcement Learning: An Overviewcited by 333
  2. 2020A Survey of Reinforcement Learning Algorithms for Dynamically Varying Environmentscited by 62
  3. 2020Blue River Controls: A toolkit for Reinforcement Learning Control Systems on Hardwarecited by 5

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 2; 2025: 2; 2026: 5.

9 papers.

  1. 2024Comparison of Model Predictive Control and Proximal Policy Optimization for a 1-DOF Helicopter Systemcited by 6
  2. 2025The Crucial Role of Problem Formulation in Real-World Reinforcement Learningcited by 6
  3. 2025Multi-Objective Reinforcement Learning for Energy-Efficient Industrial Controlcited by 2
  4. 2024Deep Reinforcement Learning for Optimizing Inverter Control: Fixed and Adaptive Gain Tuning Strategies for Power System Stabilitycited by 1
  5. 2026Chebyshev Policies and the Mountain Car Problem: Reinforcement Learning for Low-Dimensional Control Taskscited by 1
  6. 2026Integrating Physics-Informed Neural Networks for Safe Reinforcement Learning in a 1-DoF Helicopter Systemcited by 0
  7. 2026Anticipatory Reinforcement Learning for Trajectory Trackingcited by 0
  8. 2026Sample-Efficient Pareto Front Modeling for Energy-Aware Reinforcement Learning Using Bayesian Optimizationcited by 0
  9. 2026Safe Reinforcement Learning using Ideas from Model Predictive Controlcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2021Sim-Env: Decoupling OpenAI Gym Environments from Simulation Models
  2. 2026SimuAgent: An LLM-Based Simulink Modeling Assistant Enhanced with Reinforcement Learning
  3. 2025SimLauncher: Launching Sample-Efficient Real-world Robotic Reinforcement Learning via Simulation Pre-training
  4. 2026Abstract Sim2Real through Approximate Information States
  5. 2025Rulebook: bringing co-routines to reinforcement learning environments
  6. 2020Sim-to-Real Transfer in Deep Reinforcement Learning for Robotics: a Survey
  7. 2025Simulus: Combining Improvements in Sample-Efficient World Model Agents
  8. 2025Towards Task-Oriented Flying: Framework, Infrastructure, and Principles