3 accepted papers
Contextual Reinforcement Learning (CRL) tackles the problem of solving a set of related Contextual Markov Decision Processes (CMDPs) that vary across different context variables. Traditional approaches---independent training and multi-task learning---struggle with either excessive computational cost
The recent development of connected and automated vehicle (CAV) technologies has spurred investigations to optimize dense urban traffic, maximizing vehicle speed and throughput. This article explores advisory autonomy, in which real-time driving advisories are issued to human drivers, thus achieving…
Deep reinforcement learning (RL) is a powerful approach to complex decision-making. However, one issue that limits its practical application is its brittleness, sometimes failing to train in the presence of small changes in the environment. Motivated by the success of zero-shot transfer—where pre-tr…