James MacGlashan
James MacGlashan
Sony AI
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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
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
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
Showing versus doing: Teaching by demonstration
MK Ho, M Littman, J MacGlashan, F Cushman, JL Austerweil
Advances in neural information processing systems 29, 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
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
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
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
Implementing the deep q-network
M Roderick, J MacGlashan, S Tellex
arXiv preprint arXiv:1711.07478, 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
Grounding English commands to reward functions
S Squire, S Tellex, D Arumugam, L Yang
Robotics: Science and Systems, 2015
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
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
Between imitation and intention learning
J MacGlashan, ML Littman
Twenty-fourth international joint conference on artificial intelligence, 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
Interactive visual clustering
M Desjardins, J MacGlashan, J Ferraioli
Proceedings of the 12th international conference on Intelligent user …, 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
Minecraft as an experimental world for AI in robotics
KC Aluru, S Tellex, J Oberlin, J MacGlashan
2015 aaai fall symposium series, 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
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
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