Article

Multi-Agent Reinforcement Learning-Based UAV Path Planning for Obstacle Avoidance in Stochastic Environments

This study proposes a centralized training with decentralized execution method based on multi-agent reinforcement learning for UAV obstacle-avoidance path planning in stochastic environments, improved from the model predictive control (MPC) idea. Each agent communicates only with the centralized planner, plans feasible paths through extended observations based on a distance-weighted mean field approach, and uses multi-step value convergence to improve training efficiency. Comparative, ablation, and real-machine experiments verified the method's effectiveness and generalization performance.

Part of the event: Proposes multi-agent reinforcement learning method for UAV obstacle-avoidance path planning

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