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Workshop at AAAI-2025
This workshop will explore the challenges and opportunities of Multi-Agent Reinforcement Learning (MARL) in the context of autonomous transportation systems. It aims to address critical issues such as coordination, cooperation, scalability, and real-time decision-making among multiple autonomous agents in complex, real-world transportation environments. The workshop will cover topics including traffic optimization, fleet management, and intelligent infrastructure, bringing together experts from academia and industry to discuss the latest advancements and practical applications of MARL.
The topics of interest include, but are not limited to:
1. Multi-Agent Reinforcement Learning (MARL) algorithms for autonomous transportation systems.
2. Cooperative and competitive MARL in the context of autonomous transportation.
3. MARL for traffic optimization, congestion management, and intelligent routing.
4. MARL approaches for fleet management and autonomous vehicle coordination.
5. MARL-based decision-making and planning in autonomous transportation.
6. Communication and coordination mechanisms in MARL for autonomous agents.
7. Transfer learning and generalization in MARL for diverse transportation scenarios.
8. Safe and ethical considerations in MARL algorithms for autonomous transportation.
9. Scalability and real-time adaptation of MARL algorithms in transportation systems.
10. Hybrid approaches combining MARL with other techniques (e.g., deep learning, game theory) in autonomous transportation.
11. Simulation and real-world deployment of MARL algorithms in transportation systems.
12. Case studies and practical applications of MARL in autonomous transportation, including urban mobility, logistics, ride-sharing, and intelligent transportation systems.
13. Evaluation metrics and benchmarks for assessing the performance of MARL algorithms in transportation scenarios.
14. Interactions between MARL agents and human-driven vehicles or pedestrians in autonomous transportation.
15. Robustness and resilience of MARL algorithms in the presence of uncertainties and adversarial behavior in transportation systems.
Keynote Speakers
Keynote Speakers
He is the George Westinghouse Professor of Electrical & Computer Engineering and Robotics Institute at Carnegie Mellon University, where he directs the Safety21, the US DOT National University Transportation Cente and the Metro21 Smart Cities Institute. Prof Rajkumar’s work has influenced many commercial operating systems. He was also the founder of Ottomatika Inc that delivered the software intelligence for self-driving vehicles. Ottomatika was acquired by Delphi, now Motional. His research interests include all aspects of cyber-physical systems with a particular emphasis on connected and autonomous vehicles.
Cathy Wu is an Associate Professor at MIT in LIDS, CEE, and IDSS. She holds a Ph.D. from UC Berkeley, and B.S. and M.Eng. from MIT, all in EECS, and completed a Postdoc at Microsoft Research. Her research advances machine learning for control and optimization in mobility. She is broadly interested in AI for Engineering. Cathy has received a number of awards, including the NSF CAREER, PhD dissertation awards, and publications with distinction. She serves on the Board of Governors for the IEEE ITSS, is a Program Co-chair for RLC 2025, and is an Associate Editor (or equivalent) for ICML, NeurIPS, and ICRA. She is also spearheading efforts towards reproducible research in transportation, including co-founding the RERITE Working Group.
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