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ORCIDJames MacGlashan
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2020 – today
- 2022
[j3]Peter R. Wurman
, Samuel Barrett, Kenta Kawamoto, James MacGlashan, Kaushik Subramanian, Thomas J. Walsh, Roberto Capobianco, Alisa Devlic, Franziska Eckert, Florian Fuchs, Leilani Gilpin, Piyush Khandelwal, Varun Raj Kompella, HaoChih Lin, Patrick MacAlpine, Declan Oller, Takuma Seno, Craig Sherstan, Michael D. Thomure, Houmehr Aghabozorgi, Leon Barrett, Rory Douglas, Dion Whitehead, Peter Dürr, Peter Stone, Michael Spranger, Hiroaki Kitano:
Outracing champion Gran Turismo drivers with deep reinforcement learning. Nat. 602(7896): 223-228 (2022)- [c28]James MacGlashan, Evan Archer, Alisa Devlic, Takuma Seno, Craig Sherstan, Peter R. Wurman, Peter Stone:
Value Function Decomposition for Iterative Design of Reinforcement Learning Agents. NeurIPS 2022 - [i5]James MacGlashan, Evan Archer, Alisa Devlic, Takuma Seno, Craig Sherstan, Peter R. Wurman, Peter Stone:
Value Function Decomposition for Iterative Design of Reinforcement Learning Agents. CoRR abs/2206.13901 (2022) - 2021
- [c27]Piyush Khandelwal, James MacGlashan, Peter R. Wurman, Peter Stone:
Efficient Real-Time Inference in Temporal Convolution Networks. ICRA 2021: 13489-13495 - 2020
- [c26]Craig Sherstan, Shibhansh Dohare, James MacGlashan, Johannes Günther, Patrick M. Pilarski:
Gamma-Nets: Generalizing Value Estimation over Timescale. AAAI 2020: 5717-5725
2010 – 2019
- 2019
- [i4]Craig Sherstan, Shibhansh Dohare, James MacGlashan, Johannes Günther, Patrick M. Pilarski:
Gamma-Nets: Generalizing Value Estimation over Timescale. CoRR abs/1911.07794 (2019) - 2018
- [j2]Bei Peng, James MacGlashan, Robert Tyler Loftin, Michael L. Littman, David L. Roberts, Matthew E. Taylor:
Curriculum Design for Machine Learners in Sequential Decision Tasks. IEEE Trans. Emerg. Top. Comput. Intell. 2(4): 268-277 (2018) - 2017
- [c25]Nakul Gopalan, Marie desJardins, Michael L. Littman, James MacGlashan, Shawn Squire, Stefanie Tellex, John Winder, Lawson L. S. Wong:
Planning with Abstract Markov Decision Processes. ICAPS 2017: 480-488 - [c24]Bei Peng, James MacGlashan, Robert T. Loftin, Michael L. Littman, David L. Roberts, Matthew E. Taylor:
Curriculum Design for Machine Learners in Sequential Decision Tasks. AAMAS 2017: 1682-1684 - [c23]James MacGlashan, Mark K. Ho, Robert Tyler Loftin, Bei Peng, Guan Wang, David L. Roberts, Matthew E. Taylor, Michael L. Littman:
Interactive Learning from Policy-Dependent Human Feedback. ICML 2017: 2285-2294 - [c22]David Whitney, Eric Rosen, James MacGlashan, Lawson L. S. Wong, Stefanie Tellex:
Reducing errors in object-fetching interactions through social feedback. ICRA 2017: 1006-1013 - [i3]James MacGlashan, Mark K. Ho, Robert Tyler Loftin, Bei Peng, David L. Roberts, Matthew E. Taylor, Michael L. Littman:
Interactive Learning from Policy-Dependent Human Feedback. CoRR abs/1701.06049 (2017) - [i2]Michael L. Littman, Ufuk Topcu, Jie Fu, Charles Lee Isbell Jr., Min Wen, James MacGlashan:
Environment-Independent Task Specifications via GLTL. CoRR abs/1704.04341 (2017) - [i1]Melrose Roderick, James MacGlashan, Stefanie Tellex:
Implementing the Deep Q-Network. CoRR abs/1711.07478 (2017) - 2016
- [j1]Robert T. Loftin, Bei Peng, James MacGlashan, Michael L. Littman, Matthew E. Taylor, Jeff Huang, David L. Roberts:
Learning behaviors via human-delivered discrete feedback: modeling implicit feedback strategies to speed up learning. Auton. Agents Multi Agent Syst. 30(1): 30-59 (2016) - [c21]David Abel, James MacGlashan, Michael L. Littman:
Reinforcement Learning as a Framework for Ethical Decision Making. AAAI Workshop: AI, Ethics, and Society 2016 - [c20]Robert Tyler Loftin, James MacGlashan, Bei Peng, Matthew E. Taylor, Michael L. Littman, David L. Roberts:
Towards Behavior-Aware Model Learning from Human-Generated Trajectories. AAAI Fall Symposia 2016 - [c19]Bei Peng, James MacGlashan, Robert Tyler Loftin, Michael L. Littman, David L. Roberts, Matthew E. Taylor:
A Need for Speed: Adapting Agent Action Speed to Improve Task Learning from Non-Expert Humans. AAMAS 2016: 957-965 - [c18]Mark K. Ho, James MacGlashan, Amy Greenwald, Michael L. Littman, Elizabeth Hilliard, Carl Trimbach, Stephen Brawner, Josh Tenenbaum, Max Kleiman-Weiner, Joseph L. Austerweil:
Feature-based Joint Planning and Norm Learning in Collaborative Games. CogSci 2016 - [c17]Mark K. Ho, Michael L. Littman, James MacGlashan, Fiery Cushman, Joseph L. Austerweil:
Showing versus doing: Teaching by demonstration. NIPS 2016: 3027-3035 - 2015
- [c16]Krishna Chaitanya Aluru, Stefanie Tellex, John Oberlin, James MacGlashan:
Minecraft as an Experimental World for AI in Robotics. AAAI Fall Symposia 2015: 5-12 - [c15]David Ellis Hershkowitz, James MacGlashan, Stefanie Tellex:
Learning Propositional Functions for Planning and Reinforcement Learning. AAAI Fall Symposia 2015: 38-45 - [c14]Emily Wu, Yuxin Han, David Whitney, John Oberlin, James MacGlashan, Stefanie Tellex:
Robotic Social Feedback for Object Specification. AAAI Fall Symposia 2015: 150-157 - [c13]David Abel, D. Ellis Hershkowitz, Gabriel Barth-Maron, Stephen Brawner, Kevin O'Farrell, James MacGlashan, Stefanie Tellex:
Goal-Based Action Priors. ICAPS 2015: 306-314 - [c12]James MacGlashan, Michael L. Littman:
Between Imitation and Intention Learning. IJCAI 2015: 3692-3698 - [c11]Nicholay Topin, Nicholas Haltmeyer, Shawn Squire, John Winder, Marie desJardins, James MacGlashan:
Portable Option Discovery for Automated Learning Transfer in Object-Oriented Markov Decision Processes. IJCAI 2015: 3856-3864 - [c10]James MacGlashan, Monica Babes-Vroman, Marie desJardins, Michael L. Littman, Smaranda Muresan, Shawn Squire, Stefanie Tellex, Dilip Arumugam, Lei Yang:
Grounding English Commands to Reward Functions. Robotics: Science and Systems 2015 - 2014
- [c9]Robert Tyler Loftin, James MacGlashan, Bei Peng, Matthew E. Taylor, Michael L. Littman, Jeff Huang, David L. Roberts:
A Strategy-Aware Technique for Learning Behaviors from Discrete Human Feedback. AAAI 2014: 937-943 - [c8]Gabriel Barth-Maron, David Abel, James MacGlashan, Stefanie Tellex:
Affordances as Transferable Knowledge for Planning Agents. AAAI Fall Symposia 2014 - [c7]Marie desJardins, Tenji Tembo, Nicholay Topin, Michael Bishoff, Shawn Squire, James MacGlashan, Rose Carignan, Nicholas Haltmeyer:
Discovering Subgoals in Complex Domains. AAAI Fall Symposia 2014 - [c6]James MacGlashan, Michael L. Littman, Fiery Cushman:
Flexible theft and resolute punishment: Evolutionary dynamics of social behavior among reinforcement-learning agents. CogSci 2014 - [c5]Robert Tyler Loftin, Bei Peng, James MacGlashan, Michael L. Littman, Matthew E. Taylor, Jeff Huang, David L. Roberts:
Learning something from nothing: Leveraging implicit human feedback strategies. RO-MAN 2014: 607-612 - 2010
- [c4]James MacGlashan:
Hierarchical Skill Learning for High-Level Planning. AAAI 2010 - [c3]James MacGlashan, Don Miner, Marie desJardins:
A Game Playing System for Use in Computer Science Education. FLAIRS 2010 - [c2]Marie desJardins, James MacGlashan, Kiri L. Wagstaff:
Confidence-Based Feature Acquisition to Minimize Training and Test Costs. SDM 2010: 514-524
2000 – 2009
- 2007
- [c1]Marie desJardins, James MacGlashan, Julia Ferraioli:
Interactive visual clustering. IUI 2007: 361-364
Coauthor Index
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