MIT News AI October 2, 2026 By Michaela Jarvis | MIT Laboratory for Information and Decision Systems

Computational tools for society’s most complex challenges

Associate Professor Cathy Wu applies reinforcement learning to design computational tools that map out improvements for complex systems such as transportation. Her work demonstrates how advanced AI techniques can be used to tackle multifaceted societal challenges. By modeling these systems, she aims to identify optimal strategies for enhancing efficiency and performance.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at MIT News AI.

reinforcement-learning

arXiv AI

Jun 4

Smart Transportation Without Neurons -- Fair Metro Network Expansion with Tabular Reinforcement Learning

arXiv:2606. 04167v1 Announce Type: cross Abstract: We tackle the Metro Network Expansion Problem (MNEP), a subset of the Transport Network Design Problem (TNDP), which focuses on expanding metro systems to satisfy travel demand.

By Dimitris Michailidis, Sennay Ghebreab, Fernando P. Santos

reinforcement-learning safety

OpenAI Blog

Oct 26, 2017

Learning a hierarchy

We’ve developed a hierarchical reinforcement learning algorithm that learns high-level actions useful for solving a range of tasks, allowing fast solving of tasks requiring thousands of timesteps. Our algorithm, when applied to a set of navigation problems, discovers a set of high-level actions for walking and crawling in different directions, which enables the agent to master new navigation tasks quickly.

agents reinforcement-learning

MIT News AI

Jun 11

Jinhua Zhao named head of the Department of Urban Studies and Planning

An expert in behavioral science and transportation, Zhao combines these studies with AI and public policy to address some of the most urgent challenges facing cities.

By Maria Iacobo | School of Architecture and Planning

OpenAI Blog

Apr 5, 2018

Retro Contest

We’re launching a transfer learning contest that measures a reinforcement learning algorithm’s ability to generalize from previous experience.

reinforcement-learning fine-tuning

arXiv AI

Jun 2

TrafficClaw: A Generalizable LLM Agent in the Unified Physical Environment for Urban Traffic Control

arXiv:2604. 17456v2 Announce Type: replace Abstract: Large language model (LLM) agents have shown strong capabilities in long-horizon reasoning, tool use, and decision-making in digital environments, yet extending them to physically grounded systems remains challenging.

By Siqi Lai, Pan Zhang, Yuping Zhou, Jindong Han, Yansong Ning, Hao Liu

llms agents reinforcement-learning

arXiv Machine Learning

Aug 27

Simulating Cognitive Smart Freight Corridors with Agent-Based Models and Reinforcement Learning

The paper introduces an agent‑based modeling framework that integrates a physical infrastructure layer, a V2X connectivity layer, and a decision layer using reinforcement learning and multi‑agent reinforcement learning to simulate smart freight corridors. Three scenarios—Baseline, Assisted, and Cognitive—are evaluated on throughput, congestion, energy, emissions, and robustness, with the Cognitive scenario outperforming the baseline in throughput and congestion, and the Assisted scenario achieving energy savings via platooning. Sensitivity analysis shows that the smart corridor’s throughput advantage grows under high demand and that MARL coordination better utilizes fixed charging capacity than rule‑based methods.

By Madelaine Martinez-Ferguson, Chun Wang, Mustafa Can Camur, Xueping Li

agents reinforcement-learning efficiency