James MacGlashan

Publication Activity (10 Years)

Years Active: 2007-2022
Publications (10 Years): 20

Top Topics

Reward Function

Participatory Simulation

Complex Domains

Reinforcement Learning

Top Venues

AAAI Fall Symposia

Publications

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)
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 (2022)
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 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) (2022)
Piyush Khandelwal, James MacGlashan, Peter R. Wurman, Peter Stone
Efficient Real-Time Inference in Temporal Convolution Networks. ICRA (2021)
Craig Sherstan, Shibhansh Dohare, James MacGlashan, Johannes Günther, Patrick M. Pilarski
Gamma-Nets: Generalizing Value Estimation over Timescale. AAAI (2020)
Craig Sherstan, Shibhansh Dohare, James MacGlashan, Johannes Günther, Patrick M. Pilarski
Gamma-Nets: Generalizing Value Estimation over Timescale. CoRR (2019)
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) (2018)
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)
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)
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)
David Whitney, Eric Rosen, James MacGlashan, Lawson L. S. Wong, Stefanie Tellex
Reducing errors in object-fetching interactions through social feedback. ICRA (2017)
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 (2017)
Melrose Roderick, James MacGlashan, Stefanie Tellex
Implementing the Deep Q-Network. CoRR (2017)
Michael L. Littman, Ufuk Topcu, Jie Fu, Charles Lee Isbell Jr., Min Wen, James MacGlashan
Environment-Independent Task Specifications via GLTL. CoRR (2017)
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) (2016)
David Abel, James MacGlashan, Michael L. Littman
Reinforcement Learning as a Framework for Ethical Decision Making. AAAI Workshop: AI, Ethics, and Society (2016)
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)
Mark K. Ho, Michael L. Littman, James MacGlashan, Fiery Cushman, Joseph L. Austerweil
Showing versus doing: Teaching by demonstration. NIPS (2016)
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)
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)
David Abel, D. Ellis Hershkowitz, Gabriel Barth-Maron, Stephen Brawner, Kevin O'Farrell, James MacGlashan, Stefanie Tellex
Goal-Based Action Priors. ICAPS (2015)
Emily Wu, Yuxin Han, David Whitney, John Oberlin, James MacGlashan, Stefanie Tellex
Robotic Social Feedback for Object Specification. AAAI Fall Symposia (2015)
Krishna Chaitanya Aluru, Stefanie Tellex, John Oberlin, James MacGlashan
Minecraft as an Experimental World for AI in Robotics. AAAI Fall Symposia (2015)
David Ellis Hershkowitz, James MacGlashan, Stefanie Tellex
Learning Propositional Functions for Planning and Reinforcement Learning. AAAI Fall Symposia (2015)
James MacGlashan, Michael L. Littman
Between Imitation and Intention Learning. IJCAI (2015)
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)
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)
James MacGlashan, Michael L. Littman, Fiery Cushman
Flexible theft and resolute punishment: Evolutionary dynamics of social behavior among reinforcement-learning agents. CogSci (2014)
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)
Gabriel Barth-Maron, David Abel, James MacGlashan, Stefanie Tellex
Affordances as Transferable Knowledge for Planning Agents. AAAI Fall Symposia (2014)
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)
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)
Marie desJardins, James MacGlashan, Kiri L. Wagstaff
Confidence-Based Feature Acquisition to Minimize Training and Test Costs. SDM (2010)
James MacGlashan
Hierarchical Skill Learning for High-Level Planning. AAAI (2010)
James MacGlashan, Don Miner, Marie desJardins
A Game Playing System for Use in Computer Science Education. FLAIRS Conference (2010)
Marie desJardins, James MacGlashan, Julia Ferraioli
Interactive visual clustering. IUI (2007)