Our research studies the synthesis of reinforcement learning, convex optimization, and automated planning with applications such as robotics and intelligent transportation systems.
Deep Reinforcement Learning
What: Safely train an autonomous controller through interactions with the environment. The training process is expected to be safe, fast, and efficient. For the autonomous driving domain, we rely solely on video cameras as the controller's input.
Why: Our main goal is to foster viable autonomous transportation technology which is expected to result in a more sustainable urban society. Specifically, reduced accidents and fatalities, reduced traffic congestion, reduced CO2 emissions, increased road capacity, lower fuel consumption, increased transportation accessibility, reduced travel times, and reduced transportation costs.
How: We develop and apply state-of-the-art deep reinforcement learning algorithms for training an autonomous controller. By doing so we bypass the need to define and learn specific features from the input image. Instead, relevant features are automatically inferred through an appropriate reward function.
Who: Guni Sharon, Sheelabhadra Dey,
A demo of our Joint Imitation-Reinforcement Learning Framework (JIRL) for Safety-Critical Domains
- Task Phasing: Automated Curriculum Learning from Demonstrations.
Vaibhav Bajaj, Guni Sharon, and Peter Stone
In Proceedings of the 33rd International Conference on Automated Planning and Scheduling (ICAPS), 2023
pdf bib code - Reinforcement Learning Benchmarks for Traffic Signal Control.
James Ault and Guni Sharon
In Proceedings of the 35th Neural Information Processing Systems (NeurIPS 2021) Track on Datasets and Benchmarks, 2021
pdf bib - A Joint Imitation-Reinforcement Learning Framework for Reduced Baseline Regret.
Sheelabhadra Dey, Sumedh Pendurkar, Guni Sharon, and Josiah Hanna
In the Proceedings of the 34th International Conference on Intelligent Robots and Systems (IROS 2021), 2021
pdf bib slides demo code - Alleviating Road Traffic Congestion with Artificial Intelligence.
Guni Sharon
In Proceedings of the 30th International Joint Conference on Artificial Intelligence (IJCAI-21), Early-Career Spotlight, 2021
pdf bib - Learning an Interpretable Traffic Signal Control Policy.
James Ault, Josiah P. Hanna, and Guni Sharon
In the Proceedings of the 19th International Conference on Autonomous Agents and MultiAgent Systems (AAMAS 2020), 2020
pdf bib code
2023
2021
2020
Autonomous Intersection Controll
What: Coordinate vehicles passing through an intersection in a safe and efficient way.
Why: Travel time studies in urban areas show that 12-55% of commute travel time is due to delays induced by intersections. Hence, optimized intersection management has the potential of reducing commute time, traffic congestion, emissions, and fuel consumption, while requiring minimal infrastructure changes.
How: We develop self-learning controllers for traditional traffic signals. Such controllers utilize deep reinforcement learning algorithms to learn an optimized policy. In a different thread, we rely on the fine and accurate control of connected and autonomous vehicles along with communication capabilities to develop protocols that coordinate such vehicles to cross a given intersection in a safe and efficient way.
Who: Guni Sharon, James Ault, Aaron Parks-Young
- Intersection Management Protocol for Mixed Autonomous and Human-Operated Vehicles.
Aaron Parks-Young and Guni Sharon
In IEEE Transactions on Intelligent Transportation Systems, DOI: 10.1109/TITS.2022.3169658.
pdf bib technical report (simulation environment) code - Reinforcement Learning Benchmarks for Traffic Signal Control.
James Ault and Guni Sharon
In Proceedings of the 35th Neural Information Processing Systems (NeurIPS 2021) Track on Datasets and Benchmarks, 2021
pdf bib - Alleviating Road Traffic Congestion with Artificial Intelligence.
Guni Sharon
In Proceedings of the 30th International Joint Conference on Artificial Intelligence (IJCAI-21), Early-Career Spotlight, 2021
pdf bib - Learning an Interpretable Traffic Signal Control Policy.
James Ault, Josiah P. Hanna, and Guni Sharon
In the Proceedings of the 19th International Conference on Autonomous Agents and MultiAgent Systems (AAMAS 2020), 2020
pdf bib code - Smart Transport for Cities & Nations: The Rise of Self-Driving & Connected Vehicles.
Kara Kockelman and Stephen D. Boyles
The University of Texas at Austin, ISBN-10: 0692121501, ISBN-13: 978-0692121504, 2018
pdf hard-copy
Chapter 9: Traffic Models for Automated Vehicles.
Michael W. Levin, Guni Sharon, Stephen D. Boyles, Michael Albert, Josiah P. Hanna, Peter Stone, Rahul Patel, Hagen Fritz, Tianxin Lee, Jun Liu, Aqshems Nichols, Kara Kockelman.
Chapter 11: Application of Traffic Models.
Michael W. Levin, Guni Sharon, Stephen D. Boyles, Michael Albert, Josiah P. Hanna, Peter Stone, Rahul Patel, Hagen Fritz, Tianxin Lee, Jun Liu, Aqshems Nichols, Kara Kockelman.
- A Protocol for Mixed Autonomous and Human-Operated Vehicles at Intersections.
Guni Sharon and Peter Stone
In Autonomous Agents and Multiagent Systems - AAMAS 2017 Workshops, Best Papers, 2017
pdf slides.pptx bib - An Assessment of Autonomous Vehicles: Traffic
Impacts and Infrastructure Needs .
Dr. Kara Kockelman, Dr. Stephen Boyles, Dr. Peter Stone, Dr. Dan Fagnant, Rahul Patel, Michael W. Levin, Dr. Guni Sharon, Michele Simoni, Dr. Michael Albert, Hagen Fritz, Rebecca Hutchinson, Prateek Bansal, Gleb Domnenko, Pavle Bujanovic, Bumsik Kim, Elham Pourrahmani, Sudesh Agrawal, Tianxin Li, Josiah Hanna, Aqshems Nichols, and Dr. Jia Li
The University of Texas at Austin Center for Transportation Research., 2017
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2022
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Multiagent Pathfinding
What: In the multiagent pathfinding problem we are given a graph and a set of agents, each agent must be assigned a path leading from its initial location to its destination such that it will not collide with obstacles or other moving agents.
Why: Multiagent pathfinding is relevant to many real-world applications such as video games, traffic control, robotics, aviation, automated warehouses and more.
How: We consider various AI approaches for dealing with the combinatorial state space of this problem. Specifically, we utilize techniques from the heuristic search, multiagent planning, real-time planning, and deep-learning disciplines.
Who: Guni Sharon
Warehouse management application that is modeled as multiagent pathfinding.
- Multi-robot planning with conflicts and synergies.
Yuqian Jiang, Harel Yedidsion, Shiqi Zhang, Guni Sharon, and Peter Stone
In Autonomous Robots, 2019
pdf bib - Multirobot symbolic planning under temporal uncertainty.
Shiqi Zhang, Yuqian Jiang, Guni Sharon, and Peter Stone
In the Proceedings of the 16th Conference on Autonomous Agents and MultiAgent Systems (AAMAS 17), 2017
pdf bib - Search-Based Optimal Solvers for the Multi-Agent Pathfinding Problem: Summary and Challenges.
Ariel Felner, Stern Roni, Eyal Shimony, Eli Boyarski, Meir Goldenerg, Guni Sharon, Nathan R. Sturtevant, Glenn Wagner, and Pavel Surynek
In the Proceedings of the 10th Annual Symposium on Combinatorial Search (SOCS 17), 2017
pdf bib - Multi-Agent Path Finding with Payload Transfers and the Package-Exchange Robot-Routing Problem.
Hang Ma, Craig Tovey, Guni Sharon, T. K. Satish Kumar, and Sven Koenig
In the Proceedings of the 30th AAAI Conference on Artificial Intelligence (AAAI-16), 2016
pdf bib - Conflict-based search for optimal multi-agent pathfinding.
Guni Sharon, Roni Stern, Ariel Felner, and Nathan R. Sturtevant
In Artificial Intelligence 219 (2015): 40-66., 2015
Preliminary version appeared in the Proceedings of the 26th AAAI Conference on Artificial Intelligence (AAAI-12)
pdf-AIJ15 bib-AIJ15
pdf-AAAI12 bib-AAAI12 - ICBS Improved Conflict-based Search algorithm
for Multi-Agent Pathfinding.
Eli Boyarski, Ariel Felner, Guni Sharon, and Roni Stern
In the Proceedings of the 24th International Joint Conference (IJCAI 15), 2015
pdf bib - Multi-Agent Pathfinding as a Combinatorial Auction.
Ofra Amir, Guni Sharon, and Roni Stern
In the Proceedings of the 29th AAAI Conference on Artificial Intelligence (AAAI-15), 2015
pdf bib - Don't Split, try to Work It Out: Bypassing
Conflicts in Multi-Agent Pathfinding.
Eli Boyarski, Ariel Felner, Guni Sharon, and Roni Stern
In the Proceedings of the 25th International Conference on Automated Planning and Scheduling (ICAPS 15), 2015
pdf bib - Suboptimal variants of the conflict-based search algorithm for the multi-agent pathfinding problem.
Max Barer, Guni Sharon, Roni Stern, and Ariel Felner
In the Proceedings of the 7th Annual Symposium on Combinatorial Search (SOCS 14), 2014
pdf bib -
The Increasing Cost Tree Search for Optimal Multi-agent Pathfinding.
Guni Sharon, Roni Stern, Meir Goldenberg, and Ariel Felner
In Artificial Intelligence 195 (2013): 470-495., 2013
Preliminary version appeared in the Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI-2011)
pdf-AIJ13 bib-AIJ13
pdf-IJCAI11 bib-AIJ13 - Meta-Agent Conflict-Based Search for Optimal Multi-Agent Path Finding.
Guni Sharon, Roni Stern, Ariel Felner, Nathan R Sturtevant
In the Proceedings of the 4th Annual Symposium on Combinatorial Search (SOCS 12), 2012
Awarded best paper in SOCS 2012
pdf bib - Pruning Techniques for the Increasing
Cost Tree Search for Optimal Multi-Agent Pathfinding.
Guni Sharon, Roni Stern, Meir Goldenberg, and Ariel Felner
In the Proceedings of the 3ed Annual Symposium on Combinatorial Search (SOCS 11), 2011
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Traffic Flow Optimization
What: We investigate methods by which drivers can be encouraged to follow routes that, collectively, will lead to the most efficient road usage.
Why: Growing populations in many metropolitan areas have led to noticeable rises in traffic congestion. Left to their own devices, self-interested drivers reach a user equilibrium that is far worse for the system than the ideal centralized solution that maximizes social welfare.
How: We examine game-theoretical approaches for modeling drivers' behavior and route choice. We combine various AI and convex optimization techniques to compute the optimal route assignment and the level of incentive required in order to induce optimized system performance.
Who: Guni Sharon
Delta-tolling, a practical method for computing marginal-cost pricing in traffic networks.
- Alleviating Road Traffic Congestion with Artificial Intelligence.
Guni Sharon
In Proceedings of the 30th International Joint Conference on Artificial Intelligence (IJCAI-21), Early-Career Spotlight, 2021
pdf bib - Selecting Compliant Agents for Opt-in Micro-Tolling.
Josiah P. Hanna, Guni Sharon, Steve D. Boyles and Peter Stone
In the Proceedings of the 33rd AAAI Conference on Artificial Intelligence (AAAI-19), 2019
pdf bib - Marginal Cost Pricing with a Fixed Error Factor in Traffic Networks.
Guni Sharon, Stephen D. Boyles, Shani Alkoby, and Peter Stone
In the Proceedings of the 18th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2019), 2019
pdf bib - Smart Transport for Cities & Nations: The Rise of Self-Driving & Connected Vehicles.
Kara Kockelman and Stephen D. Boyles
The University of Texas at Austin, ISBN-10: 0692121501, ISBN-13: 978-0692121504, 2018
pdf hard-copy
Chapter 12: Implementation of Dynamic Micro-Tolling.
Michael W. Levin, Guni Sharon, Stephen D. Boyles, Michael Albert, Josiah P. Hanna, Peter Stone, Rahul Patel, Hagen Fritz, Tianxin Lee.
- Enhanced Delta-tolling: Traffic Optimization via Policy Gradient Reinforcement Learning.
Hamid Mirzaei, Guni Sharon, Stephen D. Boyles, Tony Givargis, and Peter Stone
In the Proceedings of the 21st International Conference on Intelligent Transportation Systems (ITSC-18), 2018
pdf bib - Traffic Optimization for a Mixture of Self-interested and Compliant Agents.
Guni Sharon, Michael Albert, Tarun Rambha, Stephen Boyles and Peter Stone
In the Proceedings of the 32nd AAAI Conference on Artificial Intelligence (AAAI-18), 2018
pdf bib - DyETC: Dynamic Electronic Toll Collection for Traffic Congestion Alleviation.
Haipeng Chen, Bo An, Guni Sharon, Josiah P. Hanna and Peter Stone
In the Proceedings of the 32nd AAAI Conference on Artificial Intelligence (AAAI-18), 2018
pdf bib - Network-wide Adaptive Tolling for Connected and Automated Vehicles.
Guni Sharon, Michael W. Levin, Josiah P. Hanna, Tarun Rambha, Stephen D. Boyles and Peter Stone
In Transportation Research Part C, September, 2017
pdf bib - Real-time Adaptive Tolling Scheme for Optimized Social
Welfare in Traffic Networks.
Guni Sharon, Josiah P. Hanna, Tarun Rambha, Michael W. Levin, Michael Albert, Stephen D. Boyles, and Peter Stone
In the Proceedings of the 16th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2017), 2017
pdf bib - An Assessment of Autonomous Vehicles: Traffic
Impacts and Infrastructure Needs .
Dr. Kara Kockelman, Dr. Stephen Boyles, Dr. Peter Stone, Dr. Dan Fagnant, Rahul Patel, Michael W. Levin, Dr. Guni Sharon, Michele Simoni, Dr. Michael Albert, Hagen Fritz, Rebecca Hutchinson, Prateek Bansal, Gleb Domnenko, Pavle Bujanovic, Bumsik Kim, Elham Pourrahmani, Sudesh Agrawal, Tianxin Li, Josiah Hanna, Aqshems Nichols, and Dr. Jia Li
The University of Texas at Austin Center for Transportation Research., 2017
pdf bib - Bringing Smart Transport to Texans: Ensuring the Benefits of a Connected and Autonomous Transport System in Texas.
Dr. Kara Kockelman, Dr. Stephen Boyles, Paul Avery, Dr. Christian Claudel, Lisa Loftus-Otway, Dr. Daniel Fagnant, Prateek Bansal, Michael W. Levin, Dr. Yong Zhao, Dr. Jun Liu, Lewis Clements, Wendy Wagner, Dr. Duncan Stewart, Dr. Guni Sharon, Dr. Michael Albert, Dr. Peter Stone, Josiah Hanna, Rahul Patel, Hagen Fritz, Tejas Choudhary, Tianxin Li, Aqshems Nichols, Kapil Sharma, and Michele Simoni
The University of Texas at Austin Center for Transportation Research., 2017
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