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arXiv Machine Learning By Qizhen Wu, Kexin Liu, Lei Chen, Jinhu L\"u

Multi-Agent Reinforcement Learning-Based UAV Pathfinding for Obstacle Avoidance in Stochastic Environment

Read the original on arXiv Machine Learning →

The paper proposes a centralized training with decentralized execution framework for multi‑agent UAV pathfinding in stochastic environments, leveraging multi‑agent reinforcement learning enhanced by model predictive control concepts. Agents communicate only with a centralized planner and use a distance‑weighted mean‑field observation to plan feasible paths, while multi‑step value convergence improves training efficiency. Experiments, including ablation and real‑robot studies, demonstrate the method’s effectiveness and generalization.

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.

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