Model-free Learning of Corridor Clearance: A Near-term Deployment Perspective

no code implementations • 16 Dec 2023 • Dajiang Suo, Vindula Jayawardana, Cathy Wu

To overcome these challenges and enhance real-world applicability in near-term, we propose a model-free approach employing deep reinforcement learning (DRL) for designing CAV control strategies, showing its reduced overhead in designing and greater scalability and performance compared to model-based methods.

The Impact of Task Underspecification in Evaluating Deep Reinforcement Learning

no code implementations • 16 Oct 2022 • Vindula Jayawardana, Catherine Tang, Sirui Li, Dajiang Suo, Cathy Wu

We show that in comparison to evaluating DRL methods on select MDP instances, evaluating the MDP family often yields a substantially different relative ranking of methods, casting doubt on what methods should be considered state-of-the-art.

Decision Making reinforcement-learning +1

The Braess Paradox in Dynamic Traffic

no code implementations • 7 Mar 2022 • Dingyi Zhuang, Yuzhu Huang, Vindula Jayawardana, Jinhua Zhao, Dajiang Suo, Cathy Wu

The Braess's Paradox (BP) is the observation that adding one or more roads to the existing road network will counter-intuitively increase traffic congestion and slow down the overall traffic flow.