arXiv Machine Learning

BVR Sim: An Open and High-Throughput Environment for Heterogeneous Air-Combat Reinforcement Learning

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.

arXiv AI

Oct 28

Autonomous fixed-wing interception using reinforcement learning

The paper presents a fixed‑wing interceptor drone that uses a neural network trained with reinforcement learning to map monocular vision inputs directly to actuator commands. The policy, developed in a custom Python environment with proximal policy optimization, was tested against classical guidance controllers and demonstrated superior performance in simulation and successful interception of maneuvering fixed‑wing targets in real flight. This work illustrates the feasibility of AI‑driven autonomous control for long‑endurance interception missions.

By Daan van Hamersveld, Ewoud Smeur, Iurii Kriachko

agents reinforcement-learning

arXiv Machine Learning

Jul 21

Hierarchical Reinforcement Learning for Air Combat at DARPA's AlphaDogfight Trials

arXiv:2105. 00990v3 Announce Type: replace Abstract: Autonomous control in high-dimensional, continuous state spaces is a persistent and important challenge in the fields of robotics and artificial intelligence.

By Adrian P. Pope, Jaime S. Ide, Daria Micovic, Henry Diaz, David Rosenbluth, Lee Ritholtz, Jason C. Twedt, Thayne T. Walker, Kevin Alcedo, Daniel Javorsek

agents reinforcement-learning robotics

arXiv AI

Oct 28

Curriculum-Driven Deep Reinforcement Learning for Autonomous Far-Range Rocket Landing

The paper introduces a curriculum-driven deep reinforcement learning framework for autonomous far-range rocket landing. It uses a three-stage curriculum, critic warm‑up, rehearsal to avoid catastrophic forgetting, and a high‑dimensional observation vector with a full 3×3 rotation matrix. The redesigned reward function bounds per‑step rewards and accumulates progress, yielding a 99.98% success rate over 100,000 episodes, far surpassing the baseline.

By Atin Chowdhury, Piyush Joshi

agents reinforcement-learning benchmarks

arXiv Machine Learning

Aug 17

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

arXiv:2608. 14135v1 Announce Type: cross Abstract: Autonomous pursuit-evasion is a fundamental challenge for Unmanned Aerial Vehicles (UAVs), requiring rapid decision-making under tightly coupled dynamics and continuously changing opponent behaviors.

By Wenhao Tang, Tianyang Chen, Zhejun Cui, Boyuan An, Jiayu Chen, Ruize Zhang, Huidong Liu, Tianyue Wu, Qingmin Liao, Fei Gao, Yu Wang, Chao Yu

agents reinforcement-learning robotics

arXiv Machine Learning

Sep 14

Curriculum-Based Adversarial Heterogeneous Agent Reinforcement Learning for Autonomous Quad-Copter Landing in Maritime Settings

The paper presents a curriculum‑based adversarial heterogeneous agent reinforcement learning (HARL‑AC) approach for autonomous quad‑copter landing on a ship deck in maritime settings. Using Heterogeneous‑Agent Proximal Policy Optimization (HAPPO) in NVIDIA Isaac Lab, the authors train a cooperative control policy that outperforms domain‑randomized baselines, achieving up to 97.5% success on in‑distribution sea states and higher median success and lower crash rates on out‑of‑distribution sea states. The adversarially trained policy also exhibits more cautious behavior, slightly increasing timeouts but improving safety in severe, unseen conditions.

By Allan Minh-Tam Nguyen, Sree Showrya Kotala, Stefan Banioi-Crijman, Kurt Driessens, Rico M\"ockel

agents reinforcement-learning robotics benchmarks safety

arXiv AI

Sep 21

AgenticRL: Agentic Reinforcement Learning with Self-Refinement for Complex UAV Navigation

arXiv:2606.03963v4 Announce Type: replace-cross Abstract: Deep reinforcement learning enables autonomous robots to learn complex navigation tasks, but still relies heavily on time consuming manual re...

By Roohan Ahmed Khan, Yasheerah Yaqoot, Amir Atef Habel, Muhammad Ahsan Mustafa, Dzmitry Tsetserukou

agents reinforcement-learning robotics fine-tuning multimodal safety

arXiv AI

Jul 21

Building2Building: A Large Scale Benchmark for Generalizable Real-World Reinforcement Learning

arXiv:2607. 16534v1 Announce Type: cross Abstract: Reinforcement learning (RL) has achieved strong results in control, yet learned policies remain brittle to changes in dynamics, action spaces, observation spaces, or goals, a critical limitation for real-world deployment.

By Vincent Taboga, Justin Veilleux, Doseok Jang, Anushree Rankawat, Pierre-Luc Bacon

reinforcement-learning benchmarks

arXiv AI

Jun 2

Crazyflow: An Accurate, GPU-Accelerated, Differentiable Drone Simulator in JAX

arXiv:2606. 01478v1 Announce Type: cross Abstract: High-quality, large-scale synthetic data from simulations is becoming a cornerstone for pushing the capabilities of robot algorithms.

By Martin Schuck, Marcel P. Rath, Yufei Hua, AbhisheK Goudar, SiQi Zhou, Angela P. Schoellig

agents reinforcement-learning robotics benchmarks

arXiv Computer Vision

Oct 15

Player-1: Learning to Play from Synthetic Experience

arXiv:2610.20584v1 Announce Type: new Abstract: Computer-use agents have demonstrated strong capabilities in interpreting screens and executing keyboard and mouse actions. However, games, with their...

By Mingyu Ouyang, Yiran Wang, Siyuan Hu, Xingyilang Yin, Huiqiang Sun, Guangzhi Wang, Junfu Pu, Cheng Cheng, Honghao Chen, Ying Shan, Mike Zheng Shou, Hwee Tou Ng

agents reinforcement-learning fine-tuning

arXiv Machine Learning

Oct 7

HARL-A: An Extensible Benchmark Framework for Heterogeneous Multi-Agent Adversarial Reinforcement Learning in IsaacLab

arXiv:2510.01264v2 Announce Type: replace Abstract: Progress in adversarial multi-agent reinforcement learning (MARL) for robotics has been hampered by a lack of shared, extensible infrastructure tha...

By Isaac Peterson, Christopher Allred, Jacob Morrey, Mario Harper

agents reinforcement-learning robotics benchmarks safety

arXiv AI

Sep 12

DRG-MAPPO: Hierarchical Dynamic Role-Graph Multi-Agent Reinforcement Learning for Cooperative Air Combat

The paper introduces DRG-MAPPO, a hierarchical multi‑agent reinforcement learning framework for cooperative air combat. It combines graph‑based relational modeling with dynamic role assignment, using a high‑level policy to allocate tactical roles such as leader and supporter, and a low‑level policy to execute maneuver actions. The approach includes a target‑priority auxiliary task and achieves an 87% win rate in experiments, indicating effective coordination and stability.

By Junlin Liu, Chengwei Li, Yang Gao, Hui Chang, Xinchen Zhang, Zhijun Zhao, Hao Zhao

agents reinforcement-learning benchmarks safety

arXiv Machine Learning

Jul 24

Conflict Resolution under Degraded Surveillance in Air Corridors Using Multi-Agent Reinforcement Learning

arXiv:2607. 20547v1 Announce Type: new Abstract: Safe Advanced Air Mobility operations require aircraft to maintain separation when surveillance information is noisy, delayed, incomplete, or temporarily unavailable.

By Esrat Farhana Dulia, Syed Arbab Mohd Shihab, Caleb Adams, Ruben Del Rosario

agents reinforcement-learning safety

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