Taylor, Matthew E.

55 publications

AAAI 2025 An LLM-Guided Tutoring System for Social Skills Training Michael Guevarra, Indronil Bhattacharjee, Srijita Das, Christabel Wayllace, Carrie Demmans Epp, Matthew E. Taylor, Alan Tay

ICLR 2025 Leveraging Sub-Optimal Data for Human-in-the-Loop Reinforcement Learning Calarina Muslimani, Matthew E. Taylor

ICML 2025 Model-Based Exploration in Monitored Markov Decision Processes Alireza Kazemipour, Matthew E. Taylor, Michael Bowling

IJCAI 2025 The Evolving Landscape of LLM- and VLM-Integrated Reinforcement Learning Sheila Schoepp, Masoud Jafaripour, Yingyue Cao, Tianpei Yang, Fatemeh Abdollahi, Shadan Golestan, Zahin Sufiyan, Osmar R. Zaïane, Matthew E. Taylor

AAAI 2024 A Transfer Approach Using Graph Neural Networks in Deep Reinforcement Learning Tianpei Yang, Heng You, Jianye Hao, Yan Zheng, Matthew E. Taylor

NeurIPSW 2024 Analyzing Reward Functions via Trajectory Alignment Calarina Muslimani, Suyog Chandramouli, Serena Booth, W. Bradley Knox, Matthew E. Taylor

TMLR 2024 Conservative Evaluation of Offline Policy Learning Hager Radi Abdelwahed, Josiah P. Hanna, Matthew E. Taylor

JAIR 2024 Human-in-the-Loop Reinforcement Learning: A Survey and Position on Requirements, Challenges, and Opportunities Carl Orge Retzlaff, Srijita Das, Christabel Wayllace, Payam Mousavi, Mohammad Afshari, Tianpei Yang, Anna Saranti, Alessa Angerschmid, Matthew E. Taylor, Andreas Holzinger

AAAI 2024 PORTAL: Automatic Curricula Generation for Multiagent Reinforcement Learning Jizhou Wu, Jianye Hao, Tianpei Yang, Xiaotian Hao, Yan Zheng, Weixun Wang, Matthew E. Taylor

AAAI 2023 Augmenting Flight Training with AI to Efficiently Train Pilots Michael Guevarra, Srijita Das, Christabel Wayllace, Carrie Demmans Epp, Matthew E. Taylor, Alan Tay

IJCAI 2023 Can You Improve My Code? Optimizing Programs with Local Search Fatemeh Abdollahi, Saqib Ameen, Matthew E. Taylor, Levi H. S. Lelis

NeurIPSW 2023 Curriculum Learning for Cooperation in Multi-Agent Reinforcement Learning Rupali Bhati, SaiKrishna Gottipati, Clodéric Mars, Matthew E. Taylor

TMLR 2023 Learning Representations for Pixel-Based Control: What Matters and Why? Manan Tomar, Utkarsh Aashu Mishra, Amy Zhang, Matthew E. Taylor

AAAI 2023 Learning to Shape Rewards Using a Game of Two Partners David Mguni, Taher Jafferjee, Jianhong Wang, Nicolas Perez Nieves, Wenbin Song, Feifei Tong, Matthew E. Taylor, Tianpei Yang, Zipeng Dai, Hui Chen, Jiangcheng Zhu, Kun Shao, Jun Wang, Yaodong Yang

AAAI 2023 Model AI Assignments 2023 Todd W. Neller, Raechel Walker, Olivia Dias, Zeynep Yalçin, Cynthia Breazeal, Matthew E. Taylor, Michele Donini, Erin J. Talvitie, Charlie Pilgrim, Paolo Turrini, James Maher, Matthew Boutell, Justin Wilson, Narges Norouzi, Jonathan Scott

IJCAI 2023 Multi-Agent Advisor Q-Learning (Extended Abstract) Sriram Ganapathi Subramanian, Matthew E. Taylor, Kate Larson, Mark Crowley

NeurIPSW 2023 PADDLE: Logic Program Guided Policy Reuse in Deep Reinforcement Learning Hao Zhang, Tianpei Yang, Yan Zheng, Jianye Hao, Matthew E. Taylor

TMLR 2023 Reinforcement Teaching Calarina Muslimani, Alex Lewandowski, Dale Schuurmans, Matthew E. Taylor, Jun Luo

TMLR 2023 Two-Level Actor-Critic Using Multiple Teachers Su Zhang, Srijita Das, Sriram Ganapathi Subramanian, Matthew E. Taylor

ICMLW 2023 Video-Guided Skill Discovery Manan Tomar, Dibya Ghosh, Vivek Myers, Anca Dragan, Matthew E. Taylor, Philip Bachman, Sergey Levine

AAAI 2022 Decentralized Mean Field Games Sriram Ganapathi Subramanian, Matthew E. Taylor, Mark Crowley, Pascal Poupart

NeurIPSW 2022 Do as You Teach: A Multi-Teacher Approach to Self-Play in Deep Reinforcement Learning Chaitanya Kharyal, Tanmay Kumar Sinha, SaiKrishna Gottipati, Srijita Das, Matthew E. Taylor

JAIR 2022 Multi-Agent Advisor Q-Learning Sriram Ganapathi Subramanian, Matthew E. Taylor, Kate Larson, Mark Crowley

ICML 2022 PMIC: Improving Multi-Agent Reinforcement Learning with Progressive Mutual Information Collaboration Pengyi Li, Hongyao Tang, Tianpei Yang, Xiaotian Hao, Tong Sang, Yan Zheng, Jianye Hao, Matthew E. Taylor, Wenyuan Tao, Zhen Wang

AAAI 2021 Towered Actor Critic for Handling Multiple Action Types in Reinforcement Learning for Drug Discovery Sai Krishna Gottipati, Yashaswi Pathak, Boris Sattarov, Sahir, Rohan Nuttall, Mohammad Amini, Matthew E. Taylor, Sarath Chandar

JMLR 2020 Curriculum Learning for Reinforcement Learning Domains: A Framework and Survey Sanmit Narvekar, Bei Peng, Matteo Leonetti, Jivko Sinapov, Matthew E. Taylor, Peter Stone

AAAI 2020 Providing Uncertainty-Based Advice for Deep Reinforcement Learning Agents (Student Abstract) Felipe Leno da Silva, Pablo Hernandez-Leal, Bilal Kartal, Matthew E. Taylor

AAAI 2020 Uncertainty-Aware Action Advising for Deep Reinforcement Learning Agents Felipe Leno da Silva, Pablo Hernandez-Leal, Bilal Kartal, Matthew E. Taylor

IJCAI 2019 Interactive Reinforcement Learning with Dynamic Reuse of Prior Knowledge from Human and Agent Demonstrations Zhaodong Wang, Matthew E. Taylor

IJCAI 2019 Metatrace Actor-Critic: Online Step-Size Tuning by Meta-Gradient Descent for Reinforcement Learning Control Kenny Young, Baoxiang Wang, Matthew E. Taylor

IJCAI 2018 Autonomously Reusing Knowledge in Multiagent Reinforcement Learning Felipe Leno da Silva, Matthew E. Taylor, Anna Helena Reali Costa

IJCAI 2018 Improving Reinforcement Learning with Human Input Matthew E. Taylor

AAAI 2017 AI Projects for Computer Science Capstone Classes (Extended Abstract) Matthew E. Taylor, Sakire Arslan Ay

IJCAI 2017 Improving Reinforcement Learning with Confidence-Based Demonstrations Zhaodong Wang, Matthew E. Taylor

ICML 2017 Interactive Learning from Policy-Dependent Human Feedback James MacGlashan, Mark K. Ho, Robert Loftin, Bei Peng, Guan Wang, David L. Roberts, Matthew E. Taylor, Michael L. Littman

IJCAI 2017 Leveraging Human Knowledge in Tabular Reinforcement Learning: A Study of Human Subjects Ariel Rosenfeld, Matthew E. Taylor, Sarit Kraus

AAAI 2017 Scalable Multitask Policy Gradient Reinforcement Learning Salam El Bsat, Haitham Bou-Ammar, Matthew E. Taylor

IJCAI 2016 Theoretically-Grounded Policy Advice from Multiple Teachers in Reinforcement Learning Settings with Applications to Negative Transfer Yusen Zhan, Haitham Bou-Ammar, Matthew E. Taylor

IJCAI 2015 Reinforcement Learning from Demonstration Through Shaping Tim Brys, Anna Harutyunyan, Halit Bener Suay, Sonia Chernova, Matthew E. Taylor, Ann Nowé

AAAI 2015 Unsupervised Cross-Domain Transfer in Policy Gradient Reinforcement Learning via Manifold Alignment Haitham Bou-Ammar, Eric Eaton, Paul Ruvolo, Matthew E. Taylor

AAAI 2014 A Strategy-Aware Technique for Learning Behaviors from Discrete Human Feedback Robert Tyler Loftin, James MacGlashan, Bei Peng, Matthew E. Taylor, Michael L. Littman, Jeff Huang, David L. Roberts

ECML-PKDD 2014 Agents Teaching Agents in Reinforcement Learning (Nectar Abstract) Matthew E. Taylor, Lisa Torrey

AAAI 2014 Combining Multiple Correlated Reward and Shaping Signals by Measuring Confidence Tim Brys, Ann Nowé, Daniel Kudenko, Matthew E. Taylor

ECML-PKDD 2013 Automatically Mapped Transfer Between Reinforcement Learning Tasks via Three-Way Restricted Boltzmann Machines Haitham Bou-Ammar, Decebal Constantin Mocanu, Matthew E. Taylor, Kurt Driessens, Karl Tuyls, Gerhard Weiss

AAAI 2010 Evolving Compiler Heuristics to Manage Communication and Contention Matthew E. Taylor, Katherine E. Coons, Behnam Robatmili, Bertrand A. Maher, Doug Burger, Kathryn S. McKinley

IJCAI 2009 DCOPs Meet the Real World: Exploring Unknown Reward Matrices with Applications to Mobile Sensor Networks Manish Jain, Matthew E. Taylor, Milind Tambe, Makoto Yokoo

JMLR 2009 Transfer Learning for Reinforcement Learning Domains: A Survey Matthew E. Taylor, Peter Stone

ECML-PKDD 2008 Transferring Instances for Model-Based Reinforcement Learning Matthew E. Taylor, Nicholas K. Jong, Peter Stone

AAAI 2007 Autonomous Inter-Task Transfer in Reinforcement Learning Domains Matthew E. Taylor

ICML 2007 Cross-Domain Transfer for Reinforcement Learning Matthew E. Taylor, Peter Stone

AAAI 2007 Representation Transfer via Elaboration Matthew E. Taylor, Peter Stone

AAAI 2007 Temporal Difference and Policy Search Methods for Reinforcement Learning: An Empirical Comparison Matthew E. Taylor, Shimon Whiteson, Peter Stone

JMLR 2007 Transfer Learning via Inter-Task Mappings for Temporal Difference Learning Matthew E. Taylor, Peter Stone, Yaxin Liu

AAAI 2006 Inter-Task Action Correlation for Reinforcement Learning Tasks Matthew E. Taylor, Peter Stone

AAAI 2005 Value Functions for RL-Based Behavior Transfer: A Comparative Study Matthew E. Taylor, Peter Stone, Yaxin Liu