mlsanityData to 14 Oct 2026

Safety-Critical Reinforcement Learning with Viability-Based Action Shielding for Hypersonic Longitudinal Flight

arXiv 2602.03968 ↗First posted 3 Feb 2026eess.SY cs.SYcited by 0

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

Abstract (arXiv)

This paper presents a safety-critical reinforcement learning framework for nonlinear dynamical systems with continuous state and input spaces operating under explicit physical constraints. Hard safety constraints are enforced independently of the reward through action shielding and reachability-based admissible action sets, ensuring that unsafe behaviors are never intentionally selected during learning or execution. To capture nominal operation and recovery behavior within a single control architecture, the state space is partitioned into safe and unsafe regions based on membership in a safety box, and a mode-dependent reward is used to promote accurate tracking inside the safe region and recovery toward it when operating outside. To enable online tabular learning on continuous dynamics, a finite-state abstraction is constructed via state aggregation, and action selection and value updates are consistently restricted to admissible actions. The framework is demonstrated on a longitudinal point-mass hypersonic vehicle model with aerodynamic and propulsion couplings, using angle of attack and throttle as control inputs.

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 9 of its 27 references that are in the map. First posted on arXiv 3 Feb 2026.

Builds on

9 of 27 references.

Closest ancestors · 1

Same direction, newest first

  1. 2025Probabilistically safe and efficient model-based reinforcement learning1 Apr

Other references · 8

  1. 2017Constrained Policy Optimizationcited by 983
  2. 2017Safe Reinforcement Learning via Shieldingcited by 449
  3. 2020A Closer Look at Invalid Action Masking in Policy Gradient Algorithmscited by 171
  4. 2019Lyapunov-based Safe Policy Optimization for Continuous Controlcited by 145
  5. 2018Reinforcement Learning for UAV Attitude Controlcited by 48
  6. 2025A Survey of Safe Reinforcement Learning and Constrained MDPs: A Technical Survey on Single-Agent and Multi-Agent Safetycited by 13
  7. 2021Adaptive Approach Phase Guidance for a Hypersonic Glider via Reinforcement Meta Learningcited by 2
  8. 2023Deep Reinforcement Learning for Weapons to Targets Assignment in a Hypersonic strikecited by 1

Built on by

Not cited yet.

Similar, not cited

Close in wording, no citation either way

  1. 2026Learning Safety-Guaranteed, Non-Greedy Control Barrier Functions Using Reinforcement Learning
  2. 2026CAPSULE: Control-Theoretic Action Perturbations for Safe Uncertainty-Aware Reinforcement Learning
  3. 2026Hamilton-Jacobi Reachability-Based Safe Reinforcement Learning for Emergency Collision Avoidance
  4. 2024Sampling-based Safe Reinforcement Learning for Nonlinear Dynamical Systems
  5. 2025A Physics-Informed Machine Learning Framework for Safe and Optimal Control of Autonomous Systems
  6. 2026Meta-Reinforcement Learning for Robust and Non-greedy Control Barrier Functions in Spacecraft Proximity Operations
  7. 2025MPC-Guided Safe Reinforcement Learning and Lipschitz-Based Filtering for Structured Nonlinear Systems
  8. 2024Handling Long-Term Safety and Uncertainty in Safe Reinforcement Learning