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Life sciences · Preprint
arXiv · August 14, 2026
Early or partial results. Treat as a signal, not a conclusion.
AgilePE is a reinforcement learning system for autonomous UAV pursuit-evasion that integrates self-play training (using Prioritized Fictitious Self-Play and diverse opponent pools) with hardware-aligned simulation and zero-shot real-world deployment. The authors demonstrate that learned policies reproduce sophisticated aerial tactics in both simulation and on real quadrotors, but the work lacks quantitative performance metrics, comparator baselines, or statistical validation.
Descriptive systems development with simulation and real-world validation. Quadrotor unmanned aerial vehicles; no specification of platform variants, payload, or operational constraints.. Intervention: AgilePE system: self-play reinforcement learning with PFSP, diversified opponent pool, and sim-to-real transfer pipeline..
Policies map onboard state observations directly to Collective Thrust and Body Rates commands, enabling end-to-end agile maneuvering without intermediate trajectory planners or waypoint controllers. Self-play training with PFSP and diversified opponent pool enables agents to improve against historical policies while stabilizing optimization and reducing policy oscillation. Learned policies transfer zero-shot to real quadrotors without task-specific tuning.
Safety was not reported in the material analysed. Check the source before drawing any conclusion about harm.
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This is a systems paper demonstrating a proof-of-concept for autonomous UAV pursuit-evasion using reinforcement learning, with real-world validation of learned strategies but no comparative performance metrics or external benchmarking.
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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. Traditional rule-based or differential-game approaches often struggle with high-dimensional aerial interactions and agile maneuvering. We present AgilePE, a complete system for autonomous UAV pursuit-evasion via self-play reinforcement learning. AgilePE integrates agile low-level control, competitive policy optimization, and sim-to-real deployment in a unified framework. The policy directly maps onboard state observations to Collective Thrust and Body Rates (CTBR) commands, enabling end-to-end agile maneuvering without intermediate trajectory planners or waypoint controllers. For training, we use competitive self-play with Prioritized Fictitious Self-Play (PFSP) and a diversified opponent pool, enabling agents to improve against historical policies while stabilizing optimization and reducing policy oscillation. This process leads to the emergence of sophisticated pursuit and evasion strategies. For real-world deployment, we develop a hardware-aligned simulation pipeline that models actuator-response dynamics, communication latency, and domain randomization. The learned policies transfer zero-shot to real quadrotors without task-specific tuning. Real-world experiments reproduce pursuit-evasion tactics observed in simulation, including rapid dodging and flanking, and demonstrate interactive two-agent zero-shot deployment.
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