arXiv Machine Learning June 19, 2026 By Romain Poletti, Lorenzo Schena, Lilla Koloszar, Joris Degroote, Miguel Alfonso Mendez

Reinforcement Twinning for Hybrid Control of Flapping-Wing Drones

Read the original on arXiv Machine Learning →

arXiv:2505. 18201v2 Announce Type: replace-cross Abstract: Controlling flapping-wing drones requires controllers that handle time-varying, nonlinear, underactuated dynamics from incomplete, noisy sensor data.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

arXiv AI

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Control of a Twin Rotor using Twin Delayed Deep Deterministic Policy Gradient (TD3)

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arXiv Machine Learning

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Physics-enhanced reinforcement learning for real-time optimal control of dynamical systems

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AgilePE: Autonomous UAV Pursuit-Evasion via Self-Play Reinforcement Learning

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An integrated interpretable control effectiveness learning and nonlinear control allocation methodology for overactuated aircrafts

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BVR Sim is an open‑source, Gymnasium‑style environment for heterogeneous air‑combat reinforcement learning, supporting multiple JSBSim aircraft models (F‑15, F‑16, F/A‑18, F‑22) with configurable weapons, sensors, and opponents. It offers a unified tactical action interface, interchangeable Python and accelerated C++ backends, entity‑oriented observations, compositional rewards, scripted opponents, replay and visualization, and adapters for multi‑agent learning frameworks. At a 0.4‑second decision interval, the C++ backend achieves 104 simulated seconds per wall‑clock second in 1‑vs‑1 and remains practical through 10‑vs‑10 scenarios, and a policy trained on the F‑16 transfers to four unseen aircraft with a 45.5% mean win rate after controller adaptation.

By Haocheng Sun (Beijing University of Posts,Telecommunications), Mulai Tan (Air Force Engineering University)

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