MAGNNET: 深層強化学習を用いた自律走行車のためのマルチエージェントグラフニューラルネットワークによる効率的なタスク割り当て
MAGNNET: Multi-Agent Graph Neural Network-based Efficient Task Allocation for Autonomous Vehicles with Deep Reinforcement Learning
通信制約下の異種マルチエージェント系で、グラフニューラルネットワークと集中訓練・分散実行型のマルチエージェント強化学習を組み合わせ、UAVとUGVが衝突なく動的にタスクを割り当てる枠組みを提案した。
著者: Lavanya Ratnabala, Aleksey Fedoseev, Robinroy Peter, Dzmitry Tsetserukou
分類: cs.RO, cs.LG, cs.MA
原文アブストラクト
This paper addresses the challenge of decentralized task allocation within heterogeneous multi-agent systems operating under communication constraints. We introduce a novel framework that integrates graph neural networks (GNNs) with a centralized training and decentralized execution (CTDE) paradigm, further enhanced by a tailored Proximal Policy Optimization (PPO) algorithm for multi-agent deep reinforcement learning (MARL). Our approach enables unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs) to dynamically allocate tasks efficiently without necessitating central coordination in a 3D grid environment. The framework minimizes total travel time while simultaneously avoiding conflicts in task assignments. For the cost calculation and routing, we employ reservation-based A* and R* path planners. Experimental results revealed that our method achieves a high 92.5% conflict-free success rate, with only a 7.49% performance gap compared to the centralized Hungarian method, while outperforming the heuristic decentralized baseline based on greedy approach. Additionally, the framework exhibits scalability with up to 20 agents with allocation processing of 2.8 s and robustness in responding to dynamically generated tasks, underscoring its potential for real-world applications in complex multi-agent scenarios.
