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Found 4 papers, 0 papers with code
Toward Dependency Dynamics in Multi-Agent Reinforcement Learning for Traffic Signal Control
Nevertheless, this decentralized method introduces new challenges: the environment becomes partially observable from the perspective of each local agent due to constrained inter-agent communication.
Multi-agent Reinforcement Learning
Reinforcement Learning (RL)
+1
Communication Strategy on Macro-and-Micro Traffic State in Cooperative Deep Reinforcement Learning for Regional Traffic Signal Control
Next, based on the evolution equation, we propose two GAT-Aggregated (GA2) communication modules--GA2-Naive and GA2-Aug to extract both intra-region and inter-region correlations between macro and micro traffic states.
A Dynamic Temporal Self-attention Graph Convolutional Network for Traffic Prediction
Accurate traffic prediction in real time plays an important role in Intelligent Transportation System (ITS) and travel navigation guidance.
The Direction-Aware, Learnable, Additive Kernels and the Adversarial Network for Deep Floor Plan Recognition
Besides, an adversarial network with two discriminators is proposed to further improve the accuracy of the elements and to reduce the noise of the semantic segmentation.
