| Outracing champion Gran Turismo drivers with deep reinforcement learning PR Wurman, S Barrett, K Kawamoto, J MacGlashan, K Subramanian, ... Nature 602 (7896), 223-228, 2022 | 353 | 2022 |
| Interactive learning from policy-dependent human feedback J MacGlashan, MK Ho, R Loftin, B Peng, G Wang, DL Roberts, ME Taylor, ... International conference on machine learning, 2285-2294, 2017 | 321 | 2017 |
| Reinforcement learning as a framework for ethical decision making D Abel, J MacGlashan, ML Littman Workshops at the thirtieth AAAI conference on artificial intelligence, 2016 | 163 | 2016 |
| Showing versus doing: Teaching by demonstration MK Ho, M Littman, J MacGlashan, F Cushman, JL Austerweil Advances in neural information processing systems 29, 2016 | 135 | 2016 |
| Environment-independent task specifications via GLTL ML Littman, U Topcu, J Fu, C Isbell, M Wen, J MacGlashan arXiv preprint arXiv:1704.04341, 2017 | 130 | 2017 |
| Learning behaviors via human-delivered discrete feedback: modeling implicit feedback strategies to speed up learning R Loftin, B Peng, J MacGlashan, ML Littman, ME Taylor, J Huang, ... Autonomous agents and multi-agent systems 30, 30-59, 2016 | 125 | 2016 |
| Social is special: A normative framework for teaching with and learning from evaluative feedback MK Ho, J MacGlashan, ML Littman, F Cushman Cognition 167, 91-106, 2017 | 118 | 2017 |
| Implementing the deep q-network M Roderick, J MacGlashan, S Tellex arXiv preprint arXiv:1711.07478, 2017 | 86 | 2017 |
| Reducing errors in object-fetching interactions through social feedback D Whitney, E Rosen, J MacGlashan, LLS Wong, S Tellex 2017 IEEE International Conference on Robotics and Automation (ICRA), 1006-1013, 2017 | 86 | 2017 |
| A strategy-aware technique for learning behaviors from discrete human feedback R Loftin, J MacGlashan, B Peng, M Taylor, M Littman, J Huang, D Roberts Proceedings of the AAAI Conference on Artificial Intelligence 28 (1), 2014 | 80 | 2014 |
| Grounding English commands to reward functions S Squire, S Tellex, D Arumugam, L Yang Robotics: Science and Systems, 2015 | 63 | 2015 |
| A need for speed: Adapting agent action speed to improve task learning from non-expert humans B Peng, J MacGlashan, R Loftin, ML Littman, DL Roberts, ME Taylor Proceedings of the international joint conference on autonomous agents and …, 2016 | 59 | 2016 |
| Goal-based action priors D Abel, D Hershkowitz, G Barth-Maron, S Brawner, K O'Farrell, ... Proceedings of the International Conference on Automated Planning and …, 2015 | 59 | 2015 |
| Planning with abstract Markov decision processes N Gopalan, M Littman, J MacGlashan, S Squire, S Tellex, J Winder, ... Proceedings of the International Conference on Automated Planning and …, 2017 | 48 | 2017 |
| Between imitation and intention learning J MacGlashan, ML Littman Twenty-fourth international joint conference on artificial intelligence, 2015 | 48 | 2015 |
| Interactive visual clustering M Desjardins, J MacGlashan, J Ferraioli Proceedings of the 12th international conference on Intelligent user …, 2007 | 45 | 2007 |
| Feature-based Joint Planning and Norm Learning in Collaborative Games. MK Ho, J MacGlashan, A Greenwald, ML Littman, E Hilliard, C Trimbach, ... CogSci, 2016 | 43 | 2016 |
| Minecraft as an experimental world for AI in robotics KC Aluru, S Tellex, J Oberlin, J MacGlashan 2015 aaai fall symposium series, 2015 | 32 | 2015 |
| Portable option discovery for automated learning transfer in object-oriented markov decision processes. N Topin, N Haltmeyer, S Squire, J Winder, Marie desJardins, ... IJCAI, 3856-3864, 2015 | 31 | 2015 |
| Learning something from nothing: Leveraging implicit human feedback strategies R Loftin, B Peng, J MacGlashan, ML Littman, ME Taylor, J Huang, ... The 23rd IEEE international symposium on robot and human interactive …, 2014 | 31 | 2014 |
