mlsanityData to 14 Oct 2026

Integrating Physics-Informed Neural Networks for Safe Reinforcement Learning in a 1-DoF Helicopter System

arXiv 2607.03125 ↗First posted 3 Jul 2026cs.LG cs.ROcited by 0

On the map: Deep reinforcement learning methods → Safe and constrained RL

Abstract (arXiv)

Deep reinforcement learning (DRL) offers powerful control for industrial cyber-physical systems (ICPSs), but its "black-box" exploration risks violating strict hardware safety limits. Typically, these constraints are managed through complex reward shaping. In this work-in-progress paper, we embed a differentiable physics model directly into the proximal policy optimization (PPO) actor loss function. By simulating short-horizon future trajectories during training, the policy is penalized for anticipated safety violations independent of the task-reward signal. Evaluated on a simulated 1-degree-of-freedom helicopter testbed with strict pitch constraints, our physics-informed soft regularizations substantially reduce constraint violations while maintaining reliable target tracking.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Safe and constrained RL, one of 15 directions in Deep reinforcement learning methods. Builds on 5 of its 11 references that are in the map. First posted on arXiv 3 Jul 2026.

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5 of 11 references.

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Same direction, newest first

  1. 2026Reinforcement Learning for Optimal Experiment Design in Parameter Identification of Mechatronic Systems19 May

Other references · 4

  1. 2017Proximal Policy Optimization Algorithmscited by 15,215
  2. 2019Control Barrier Functions: Theory and Applicationscited by 372
  3. 2024Python-Based Reinforcement Learning on Simulink Modelscited by 9
  4. 2024Comparison of Model Predictive Control and Proximal Policy Optimization for a 1-DOF Helicopter Systemcited by 6

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  1. 2026Safe Reinforcement Learning using Ideas from Model Predictive Control
  2. 2024Safe and Reliable Training of Learning-Based Aerospace Controllers
  3. 2026TRIDENT: Breaking the Hybrid-Safety-Physics Coupling for Provably Safe Multi-Agent Reinforcement Learning
  4. 2026Physics-Aware End-to-End Deep Reinforcement Learning for Quadcopter Control with Actuator Dynamics
  5. 2026Physics-Informed Policy Optimization via Analytic Dynamics Regularization
  6. 2026Anticipatory Reinforcement Learning for Trajectory Tracking
  7. 2025A Physics-Informed Machine Learning Framework for Safe and Optimal Control of Autonomous Systems
  8. 2024Physics-model-guided Worst-case Sampling for Safe Reinforcement Learning