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arXiv Machine Learning By Leandro Parada, Kevin Yu, Panagiotis Angeloudis

IntNet: A Communication-Driven Multi-Agent Reinforcement Learning Framework for Cooperative Autonomous Driving

Read the original on arXiv Machine Learning →

IntNet is a multi-agent reinforcement learning framework designed to improve safety in autonomous driving by integrating vehicle intent transmission and adaptive communication scheduling into a single end-to-end learning system. The model uses an observation prediction module for agents to forecast future policy outputs and share them over a vehicle-to-vehicle network, while a Graph Attention Network processes incoming messages and a scheduler optimizes the communication graph for bandwidth efficiency. Experiments in complex urban environments show that IntNet achieves better coordination, lower collision rates, and reduces communication effort by up to 60% compared to state‑of‑the‑art communicative MARL methods.

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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