4 accepted papers
When designing interventions to promote desired actions, two-stage agent heterogeneity -- encompassing both engagement with the intervention and completion of the desired action -- creates significant challenges in identifying optimal intervention policies. While this two-dimensional heterogeneity c…
Numerous solutions are proposed for the Traffic Signal Control (TSC) tasks aiming to provide efficient transportation and alleviate traffic congestion. Recently, promising results have been attained by Reinforcement Learning (RL) methods through trial and error in simulators, bringing confidence in…
Graph regression is a fundamental task that has gained significant attention in various graph learning tasks. However, the inference process is often not easily interpretable. Current explanation techniques are limited to understanding Graph Neural Network (GNN) behaviors in classification tasks, le…
The emergence of reinforcement learning (RL) methods in traffic signal control (TSC) tasks has achieved promising results. Most RL approaches require the observation of the environment for the agent to decide which action is optimal for a long-term reward. However, in real-world urban scenarios, mis…