Operations of a shared, autonomous, electric vehicle fleet: Implications of vehicle & charging infrastructure decisions TD Chen, KM Kockelman, JP Hanna Transportation Research Part A: Policy and Practice 94, 243-254, 2016 | 660 | 2016 |
Grounded action transformation for robot learning in simulation J Hanna, P Stone Proceedings of the AAAI Conference on Artificial Intelligence 31 (1), 2017 | 130 | 2017 |
Bootstrapping with models: Confidence intervals for off-policy evaluation JP Hanna, P Stone, S Niekum Proceedings of the 16th Conference on Autonomous Agents and MultiAgent …, 2017 | 83 | 2017 |
Importance sampling policy evaluation with an estimated behavior policy J Hanna, S Niekum, P Stone International Conference on Machine Learning, 2605-2613, 2019 | 72 | 2019 |
Network-wide adaptive tolling for connected and automated vehicles G Sharon, MW Levin, JP Hanna, T Rambha, SD Boyles, P Stone Transportation Research Part C: Emerging Technologies 84, 142-157, 2017 | 59 | 2017 |
Real-time adaptive tolling scheme for optimized social welfare in traffic networks G Sharon, JP Hanna, T Rambha, MW Levin, M Albert, SD Boyles, P Stone Proceedings of the 16th International Conference on Autonomous Agents and …, 2017 | 56 | 2017 |
Learning an interpretable traffic signal control policy J Ault, JP Hanna, G Sharon arXiv preprint arXiv:1912.11023, 2019 | 53 | 2019 |
Minimum cost matching for autonomous carsharing JP Hanna, M Albert, D Chen, P Stone IFAC-PapersOnLine 49 (15), 254-259, 2016 | 48 | 2016 |
Data-Efficient Policy Evaluation Through Behavior Policy Search JP Hanna, PS Thomas, P Stone, S Niekum The International Conference on Machine Learning (ICML), 2017 | 46 | 2017 |
An imitation from observation approach to transfer learning with dynamics mismatch S Desai, I Durugkar, H Karnan, G Warnell, J Hanna, P Stone Advances in Neural Information Processing Systems 33, 3917-3929, 2020 | 43 | 2020 |
An assessment of autonomous vehicles: traffic impacts and infrastructure needs K Kockelman, S Boyles, P Stone, D Fagnant, R Patel, MW Levin, ... University of Texas at Austin. Center for Transportation Research, 2017 | 39 | 2017 |
Ridm: Reinforced inverse dynamics modeling for learning from a single observed demonstration BS Pavse, F Torabi, J Hanna, G Warnell, P Stone IEEE Robotics and Automation Letters 5 (4), 6262-6269, 2020 | 34 | 2020 |
Fast and precise black and white ball detection for robocup soccer J Menashe, J Kelle, K Genter, J Hanna, E Liebman, S Narvekar, R Zhang, ... RoboCup 2017: Robot World Cup XXI 11, 45-58, 2018 | 34 | 2018 |
Grounded action transformation for sim-to-real reinforcement learning JP Hanna, S Desai, H Karnan, G Warnell, P Stone Machine Learning 110 (9), 2469-2499, 2021 | 31 | 2021 |
Dyetc: Dynamic electronic toll collection for traffic congestion alleviation H Chen, B An, G Sharon, J Hanna, P Stone, C Miao, Y Soh Proceedings of the AAAI Conference on Artificial Intelligence 32 (1), 2018 | 31 | 2018 |
Importance sampling in reinforcement learning with an estimated behavior policy JP Hanna, S Niekum, P Stone Machine Learning 110 (6), 1267-1317, 2021 | 29 | 2021 |
Interpretable goal recognition in the presence of occluded factors for autonomous vehicles JP Hanna, A Rahman, E Fosong, F Eiras, M Dobre, J Redford, ... 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems …, 2021 | 25 | 2021 |
The academic advising planning domain JT Guerin, JP Hanna, L Ferland, N Mattei, J Goldsmith Proceedings of the 3rd Workshop on the International Planning Competition at …, 2012 | 25 | 2012 |
Approximation of lorenz-optimal solutions in multiobjective markov decision processes P Perny, P Weng, J Goldsmith, J Hanna International Conference on Uncertainty in Artificial Intelligence (UAI), 2013 | 24 | 2013 |
Decoupled reinforcement learning to stabilise intrinsically-motivated exploration L Schäfer, F Christianos, JP Hanna, SV Albrecht arXiv preprint arXiv:2107.08966, 2021 | 23 | 2021 |