mlsanityData to 14 Oct 2026

An Aircraft Upset Recovery System with Reinforcement Learning

arXiv 2604.24355 ↗First posted 27 Apr 2026cs.LGcited by 0

On the map: Deep reinforcement learning methods → RL for robot navigation and UAV control

Abstract (arXiv)

This article explores the progress made in the creation of a pilot activated recovery system (PARS) for advanced jet trainers that utilizes artificial intelligence (AI) in an effort to enhance operational efficiency. The PARS model employs an advanced reinforcement learning (RL) architecture, incorporating a cutting-edge soft-actor critic (SAC) model and hyper-parameter optimization methods. Negative-g punishments and other handcrafted features remarked upon by control engineers and domain experts regarding PARS are also taken into account by the system. When evaluated by them, the AI model's behavior is deemed more desirable than that of conventional control methods.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in RL for robot navigation and UAV control, one of 15 directions in Deep reinforcement learning methods. Builds on 3 of its 22 references that are in the map. First posted on arXiv 27 Apr 2026.

Builds on

3 of 22 references.

References · 3

  1. 2018Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actorcited by 4,885
  2. 2019Optuna: A Next-generation Hyperparameter Optimization Frameworkcited by 2,501
  3. 2017A Brief Survey of Deep Reinforcement Learningcited by 483

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  1. 2026Deep Reinforcement Learning based Control Design for Aircraft Recovery from Loss-of-Control Scenario
  2. 2026Perfecting Aircraft Maneuvers with Reinforcement Learning
  3. 2024Enhanced Flight Envelope Protection: A Novel Reinforcement Learning Approach
  4. 2025A Graph-Enhanced Deep-Reinforcement Learning Framework for the Aircraft Landing Problem
  5. 2023Learning Interpretable Models of Aircraft Handling Behaviour by Reinforcement Learning from Human Feedback
  6. 2023Pilot Performance modeling via observer-based inverse reinforcement learning
  7. 2024A survey of air combat behavior modeling using machine learning
  8. 2026An Automatic Ground Collision Avoidance System with Reinforcement Learning