Contextualize Me--The Case for Context in Reinforcement Learning
While Reinforcement Learning (RL) has made great strides towards solving increasingly
complicated problems, many algorithms are still brittle to even slight environmental changes …
complicated problems, many algorithms are still brittle to even slight environmental changes …
Mitigating Metropolitan Carbon Emissions with Dynamic Eco-driving at Scale
The sheer scale and diversity of transportation make it a formidable sector to decarbonize.
Here, we consider an emerging opportunity to reduce carbon emissions: the growing …
Here, we consider an emerging opportunity to reduce carbon emissions: the growing …
Probabilistic Offline Policy Ranking with Approximate Bayesian Computation
In practice, it is essential to compare and rank candidate policies offline before real-world
deployment for safety and reliability. Prior work seeks to solve this offline policy ranking …
deployment for safety and reliability. Prior work seeks to solve this offline policy ranking …
What is a typical signalized intersection in a city? A pipeline for intersection data imputation from OpenStreetMap
A Qu, A Valiveru, C Tang, V Jayawardana… - arXiv preprint arXiv …, 2024 - arxiv.org
Signalized intersections, arguably the most complicated type of traffic scenario, are essential
to urban mobility systems. With recent advancements in intelligent transportation …
to urban mobility systems. With recent advancements in intelligent transportation …
Metacognitive AI: Framework and the Case for a Neurosymbolic Approach
Metacognition is the concept of reasoning about an agent's own internal processes and was
originally introduced in the field of developmental psychology. In this position paper, we …
originally introduced in the field of developmental psychology. In this position paper, we …
Model-Free Learning of Corridor Clearance: A Near-Term Deployment Perspective
An emerging public health application of connected and automated vehicle (CAV)
technologies is to reduce response times of emergency medical service (EMS) by indirectly …
technologies is to reduce response times of emergency medical service (EMS) by indirectly …
Temporal Transfer Learning for Traffic Optimization with Coarse-grained Advisory Autonomy
The recent development of connected and automated vehicle (CAV) technologies has
spurred investigations to optimize dense urban traffic. This paper considers advisory …
spurred investigations to optimize dense urban traffic. This paper considers advisory …
IntersectionZoo: Eco-driving for Benchmarking Multi-Agent Contextual Reinforcement Learning
Despite the popularity of multi-agent reinforcement learning (RL) in simulated and two-
player applications, its success in messy real-world applications has been limited. A key …
player applications, its success in messy real-world applications has been limited. A key …
Model-Based Transfer Learning for Contextual Reinforcement Learning
Deep reinforcement learning 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 …
one issue that limits its practical application is its brittleness, sometimes failing to train in the …
Generalizing Cooperative Eco-driving via Multi-residual Task Learning
Conventional control, such as model-based control, is commonly utilized in autonomous
driving due to its efficiency and reliability. However, real-world autonomous driving contends …
driving due to its efficiency and reliability. However, real-world autonomous driving contends …