Figure1
From: A distributed multi-vehicle pursuit scheme: generative multi-adversarial reinforcement learning
Figure 1. Architecture of DGMARL-MVP. Urban traffic environments for MVP (A) provide complex pursuit-evasion scenes and interactive environments for RL. Every pursuing vehicle targets the nearest evading vehicle and launches a collaborative pursuit. GNN-based intersecting cognition (B) couples the traffic information and multi-agent interaction features to assist GMAN boosting reinforcement learning (C) in decision-making. GMANs (D) to guide RL strategy optimization via generating dense rewards, replacing the approximation of Bellman updates. MVP: Multi-vehicle pursuit; GNNs: graph neural networks; GMAN: generative multi-adversarial network.
