Matthew E. Taylor

273 papers A* 43A 51B 3C 3Misc 3Journal 128Unranked 39

Year	Rank	Type	Title / Venue / Authors
2026	A*	`ed.`	Fortieth AAAI Conference on Artificial Intelligence, Thirty-Eighth Conference on Innovative Applications of Artificial Intelligence, Sixteenth Symposium on Educational Advances in Artificial Intelligence, AAAI 2026, Singapore, January 20-27, 2026 AAAI Sven Koenig, Chad Jenkins, Matthew E. Taylor
2026	J	`jnl`	HierarchyCraft: A Benchmark Builder for Hierarchical Reasoning. J. Open Source Softw. Mathïs Fédérico, Shang Wang, Yuxuan Li, Matthew E. Taylor
2026	J	`jnl`	Human-Interactive Robot Learning: Definition, Challenges, and Recommendations. ACM Trans. Hum. Robot Interact. Kim Baraka, Ifrah Idrees, Taylor Kessler Faulkner, Erdem Biyik, Serena Booth, Mohamed Chetouani, Daniel H. Grollman, Akanksha Saran, Emmanuel Senft, Silvia Tulli, Anna-Lisa Vollmer, Antonio Andriella, Helen Beierling, Tiffany Horter, Jens Kober, Isaac S. Sheidlower, Matthew E. Taylor, Sanne van Waveren, Xuesu Xiao
2026	J	`jnl`	Laplacian Representations for Decision-Time Planning. CoRR Dikshant Shehmar, Matthew Schlegel, Matthew E. Taylor, Marlos C. Machado
2026	J	`jnl`	Reward Learning through Ranking Mean Squared Error. CoRR Chaitanya Kharyal, Calarina Muslimani, Matthew E. Taylor
2025	—	`conf`	A Call to Arms: Automated Methods for Identifying Weapons in Social Media Analysis of Conflict Zones. WebSci Afia Abedin, Abdul Bais, Cody Buntain, Laura Courchesne, Brian McQuinn, Matthew E. Taylor, Muhib Ullah
2025	J	`jnl`	A Systematic Approach to Design Real-World Human-in-the-Loop Deep Reinforcement Learning: Salient Features, Challenges and Trade-offs. CoRR Jalal Arabneydi, Md. Saiful Islam, Srijita Das, Sai Krishna Gottipati, William Duguay, Clodéric Mars, Matthew E. Taylor, Matthew Guzdial, Antoine Fagette, Younes Zerouali
2025	J	`jnl`	An Adaptive Transition Framework for Game-Theoretic Based Takeover. CoRR Dikshant Shehmar, Matthew E. Taylor, Ehsan Hashemi
2025	A*	`conf`	An LLM-Guided Tutoring System for Social Skills Training. AAAI Michael Guevarra, Indronil Bhattacharjee, Srijita Das, Christabel Wayllace, Carrie Demmans Epp, Matthew E. Taylor, Alan Tay
2025	J	`jnl`	An LLM-Guided Tutoring System for Social Skills Training. CoRR Michael Guevarra, Indronil Bhattacharjee, Srijita Das, Christabel Wayllace, Carrie Demmans Epp, Matthew E. Taylor, Alan Tay
2025	A	`conf`	Boosting Robustness in Preference-Based Reinforcement Learning with Dynamic Sparsity. AAMAS Calarina Muslimani, Bram Grooten, Deepak Ranganatha Sastry Mamillapalli, Mykola Pechenizkiy, Decebal Constantin Mocanu, Matthew E. Taylor
2025	J	`jnl`	Do as you teach: a multi-teacher approach to self-play in deep reinforcement learning. Neural Comput. Appl. Chaitanya Kharyal, Sai Krishna Gottipati, Tanmay Kumar Sinha, Fatemeh Abdollahi, Srijita Das, Matthew E. Taylor
2025	J	`jnl`	Efficient Morphology-Aware Policy Transfer to New Embodiments. CoRR Michael Przystupa, Hongyao Tang, Martin Jägersand, Santiago Miret, Mariano Phielipp, Matthew E. Taylor, Glen Berseth
2025	A	`conf`	Empowering Generalization for Deep Reinforcement Learning via Symbolic Planning. AAMAS Tianpei Yang, Srijita Das, Christabel Wayllace, Matthew E. Taylor
2025	J	`jnl`	Human at the Center: A Framework for Human-Driven AI Development. AI Mag. Danniell Hu, Diana Acosta-Navas, Susanne Gaube, Hussein Mozannar, Matthew E. Taylor, Krishnamurthy Dvijotham, Elizabeth Bondi-Kelly
2025	J	`jnl`	Human-AI collaboration in real-world complex environment with reinforcement learning. Neural Comput. Appl. Md. Saiful Islam, Srijita Das, Sai Krishna Gottipati, William Duguay, Clodéric Mars, Jalal Arabneydi, Antoine Fagette, Matthew Guzdial, Matthew E. Taylor
2025	A*	`conf`	Leveraging Sub-Optimal Data for Human-in-the-Loop Reinforcement Learning. ICLR Calarina Muslimani, Matthew E. Taylor
2025	A*	`conf`	Model-Based Exploration in Monitored Markov Decision Processes. ICML Alireza Kazemipour, Matthew E. Taylor, Michael Bowling
2025	J	`jnl`	Model-Based Exploration in Monitored Markov Decision Processes. CoRR Alireza Kazemipour, Simone Parisi, Matthew E. Taylor, Michael Bowling
2025	J	`jnl`	Operator Learning for Power Systems Simulation. CoRR Matthew Schlegel, Matthew E. Taylor, Mostafa Farrokhabadi
2025	A	`conf`	Pilot Trainees Benefit from Modelling and Adaptive Feedback. UMAP Yalmaz Ali Abdullah, Michael Guevarra, Minghao Cai, Jialiang Yan, Matthew E. Taylor, Carrie Demmans Epp
2025	J	`jnl`	Proceedings of 1st Workshop on Advancing Artificial Intelligence through Theory of Mind. CoRR Mouad Abrini, Omri Abend, Dina M. Acklin, Henny Admoni, Gregor Aichinger, Nitay Alon, Zahra Ashktorab, Ashish Atreja, Moises Auron, Alexander Aufreiter, Raghav Awasthi, Soumya Banerjee, Joseph M. Barnby, Rhea Basappa, Severin Bergsmann, Djallel Bouneffouf, Patrick Callaghan, Marc Cavazza, Thierry Chaminade, Sonia Chernova, Mohamed Chetouani, Moumita Choudhury, Axel Cleeremans, Jacek B. Cywinski, Fabio Cuzzolin, Hokin Deng, N'yoma Diamond, Camilla Di Pasquasio, Guillaume Dumas, Max J. van Duijn, Mahapatra Dwarikanath, Qingying Gao, Ashok K. Goel, Rebecca Goldstein, Matthew C. Gombolay, Gabriel Enrique Gonzalez, Amar Halilovic, Tobias Halmdienst, Mahimul Islam, Julian Jara-Ettinger, Natalie Kastel, Renana Keydar, Ashish K. Khanna, Mahdi Khoramshahi, Jihyun Kim, MiHyeon Kim, YoungBin Kim, Senka Krivic, Nikita Krasnytskyi, Arun Kumar, JuneHyoung Kwon, Eunju Lee, Shane Lee, Peter R. Lewis, Xue Li, Yijiang Li, Michal Lewandowski, Nathan Lloyd, Matthew B. Luebbers, Dezhi Luo, Haiyun Lyu, Dwarikanath Mahapatra, Kamal Maheshwari, Mallika Mainali, Piyush Mathur, Patrick Mederitsch, Shuwa Miura, Manuel Preston de Miranda, Reuth Mirsky, Shreya Mishra, Nina Moorman, Katelyn Morrison, John Muchovej, Bernhard Nessler, Felix Nessler, Hieu Minh Jord Nguyen, Abby Ortego, Francis A. Papay, Antoine Pasquali, Hamed Rahimi, Charumathi Raghu, Amanda L. Royka, Stefan Sarkadi, Jaelle Scheuerman, Simon Schmid, Paul Schrater, Anik Sen, Zahra Sheikhbahaee, Ke Shi, Reid G. Simmons, Nishant Singh, Mason O. Smith, Ramira van der Meulen, Anthia Solaki, Haoran Sun, Viktor Szolga, Matthew E. Taylor, Travis Taylor, Sanne van Waveren, Juan David Vargas
2025	A	`conf`	Taming Multi-Agent Reinforcement Learning with Estimator Variance Reduction. AAMAS Taher Jafferjee, Juliusz Ziomek, Tianpei Yang, Zipeng Dai, Jianhong Wang, Matthew E. Taylor, Kun Shao, Jun Wang, David Mguni
2025	A*	`conf`	The Evolving Landscape of LLM- and VLM-Integrated Reinforcement Learning. IJCAI Sheila Schoepp, Masoud Jafaripour, Yingyue Cao, Tianpei Yang, Fatemeh Abdollahi, Shadan Golestan, Zahin Sufiyan, Osmar R. Zaïane, Matthew E. Taylor
2025	J	`jnl`	The Evolving Landscape of LLM- and VLM-Integrated Reinforcement Learning. CoRR Sheila Schoepp, Masoud Jafaripour, Yingyue Cao, Tianpei Yang, Fatemeh Abdollahi, Shadan Golestan, Zahin Sufiyan, Osmar R. Zaïane, Matthew E. Taylor
2025	J	`jnl`	Towards Improving Reward Design in RL: A Reward Alignment Metric for RL Practitioners. CoRR Calarina Muslimani, Kerrick Johnstonbaugh, Suyog Chandramouli, Serena Booth, W. Bradley Knox, Matthew E. Taylor
2025	J	`jnl`	hammer: Multi-level coordination of reinforcement learning agents via learned messaging. Neural Comput. Appl. Nikunj Gupta, G. Srinivasaraghavan, Swarup Mohalik, Nishant Kumar, Matthew E. Taylor
2024	J	`jnl`	A Novel Framework for Automated Warehouse Layout Generation. CoRR Atefeh Shahroudnejad, Payam Mousavi, Oleksii Perepelytsia, Sahir, David Staszak, Matthew E. Taylor, Brent Bawel
2024	A*	`conf`	A Transfer Approach Using Graph Neural Networks in Deep Reinforcement Learning. AAAI Tianpei Yang, Heng You, Jianye Hao, Yan Zheng, Matthew E. Taylor
2024	J	`jnl`	A novel framework for automated warehouse layout generation. Frontiers Artif. Intell. Atefeh Shahroudnejad, Payam Mousavi, Oleksii Perepelytsia, Sahir, David Staszak, Matthew E. Taylor, Brent Bawel
2024	—	`conf`	Alleviating the Danger Of A Single Story Through Liberatory Computing Education. RESPECT Raechel Walker, Olivia Dias, Matthew E. Taylor, Cynthia Breazeal
2024	J	`jnl`	Applying reinforcement learning to learn best net to rip and re-route in global routing. ACM Trans. Design Autom. Electr. Syst. Upma Gandhi, Erfan Aghaeekiasaraee, Sahir, Payam Mousavi, Ismail S. K. Bustany, Matthew E. Taylor, Laleh Behjat
2024	J	`jnl`	Boosting Robustness in Preference-Based Reinforcement Learning with Dynamic Sparsity. CoRR Calarina Muslimani, Bram Grooten, Deepak Ranganatha Sastry Mamillapalli, Mykola Pechenizkiy, Decebal Constantin Mocanu, Matthew E. Taylor
2024	J	`jnl`	CANDERE-COACH: Reinforcement Learning from Noisy Feedback. CoRR Yuxuan Li, Srijita Das, Matthew E. Taylor
2024	J	`jnl`	Comparing explanations in RL. Neural Comput. Appl. Brittany Davis Pierson, Dustin Arendt, John Miller, Matthew E. Taylor
2024	J	`jnl`	Conservative Evaluation of Offline Policy Learning. Trans. Mach. Learn. Res. Hager Radi Abdelwahed, Josiah P. Hanna, Matthew E. Taylor
2024	J	`jnl`	FPGA Divide-and-Conquer Placement using Deep Reinforcement Learning. CoRR Shang Wang, Deepak Ranganatha Sastry Mamillapalli, Tianpei Yang, Matthew E. Taylor
2024	A	`conf`	GLIDE-RL: Grounded Language Instruction through DEmonstration in RL. AAMAS Chaitanya Kharyal, Sai Krishna Gottipati, Tanmay Kumar Sinha, Srijita Das, Matthew E. Taylor
2024	J	`jnl`	GLIDE-RL: Grounded Language Instruction through DEmonstration in RL. CoRR Chaitanya Kharyal, Sai Krishna Gottipati, Tanmay Kumar Sinha, Srijita Das, Matthew E. Taylor
2024	J	`jnl`	Human-in-the-Loop Reinforcement Learning: A Survey and Position on Requirements, Challenges, and Opportunities. J. Artif. Intell. Res. Carl Orge Retzlaff, Srijita Das, Christabel Wayllace, Payam Mousavi, Mohammad Afshari, Tianpei Yang, Anna Saranti, Alessa Angerschmid, Matthew E. Taylor, Andreas Holzinger
2024	J	`jnl`	Investigating the Benefits of Nonlinear Action Maps in Data-Driven Teleoperation. CoRR Michael Przystupa, Gauthier Gidel, Matthew E. Taylor, Martin Jägersand, Justus H. Piater, Samuele Tosatto
2024	J	`jnl`	LaFFi: Leveraging Hybrid Natural Language Feedback for Fine-tuning Language Models. CoRR Qianxi Li, Yingyue Cao, Jikun Kang, Tianpei Yang, Xi Chen, Jun Jin, Matthew E. Taylor
2024	A	`conf`	Leveraging Sub-Optimal Data for Human-in-the-Loop Reinforcement Learning. AAMAS Calarina Muslimani, Matthew E. Taylor
2024	J	`jnl`	Leveraging Sub-Optimal Data for Human-in-the-Loop Reinforcement Learning. CoRR Calarina Muslimani, Matthew E. Taylor
2024	A	`conf`	Local Linearity is All You Need (in Data-Driven Teleoperation). IROS Michael Przystupa, Gauthier Gidel, Matthew E. Taylor, Martin Jägersand, Justus H. Piater, Samuele Tosatto
2024	A	`conf`	MaDi: Learning to Mask Distractions for Generalization in Visual Deep Reinforcement Learning. AAMAS Bram Grooten, Tristan Tomilin, Gautham Vasan, Matthew E. Taylor, A. Rupam Mahmood, Meng Fang, Mykola Pechenizkiy, Decebal Constantin Mocanu
2024	A	`conf`	Monitored Markov Decision Processes. AAMAS Simone Parisi, Montaser Mohammedalamen, Alireza Kazemipour, Matthew E. Taylor, Michael Bowling
2024	J	`jnl`	Monitored Markov Decision Processes. CoRR Simone Parisi, Montaser Mohammedalamen, Alireza Kazemipour, Matthew E. Taylor, Michael Bowling
2024	J	`jnl`	ODGR: Online Dynamic Goal Recognition. CoRR Matan Shamir, Osher Elhadad, Matthew E. Taylor, Reuth Mirsky
2024	A	`conf`	PADDLE: Logic Program Guided Policy Reuse in Deep Reinforcement Learning. AAMAS Hao Zhang, Tianpei Yang, Yan Zheng, Jianye Hao, Matthew E. Taylor
2024	A*	`conf`	PORTAL: Automatic Curricula Generation for Multiagent Reinforcement Learning. AAAI Jizhou Wu, Jianye Hao, Tianpei Yang, Xiaotian Hao, Yan Zheng, Weixun Wang, Matthew E. Taylor
2024	J	`jnl`	Video Occupancy Models. CoRR Manan Tomar, Philippe Hansen-Estruch, Philip Bachman, Alex Lamb, John Langford, Matthew E. Taylor, Sergey Levine
2023	J	`jnl`	A Call to Arms: AI Should be Critical for Social Media Analysis of Conflict Zones. CoRR Afia Abedin, Abdul Bais, Cody Buntain, Laura Courchesne, Brian McQuinn, Matthew E. Taylor, Muhib Ullah
2023	J	`jnl`	A conceptual framework for externally-influenced agents: an assisted reinforcement learning review. J. Ambient Intell. Humaniz. Comput. Adam Bignold, Francisco Cruz, Matthew E. Taylor, Tim Brys, Richard Dazeley, Peter Vamplew, Cameron Foale
2023	J	`jnl`	ASN: action semantics network for multiagent reinforcement learning. Auton. Agents Multi Agent Syst. Tianpei Yang, Weixun Wang, Jianye Hao, Matthew E. Taylor, Yong Liu, Xiaotian Hao, Yujing Hu, Yingfeng Chen, Changjie Fan, Chunxu Ren, Ye Huang, Jiangcheng Zhu, Yang Gao
2023	A*	`conf`	Augmenting Flight Training with AI to Efficiently Train Pilots. AAAI Michael Guevarra, Srijita Das, Christabel Wayllace, Carrie Demmans Epp, Matthew E. Taylor, Alan Tay
2023	A	`conf`	Automatic Noise Filtering with Dynamic Sparse Training in Deep Reinforcement Learning. AAMAS Bram Grooten, Ghada Sokar, Shibhansh Dohare, Elena Mocanu, Matthew E. Taylor, Mykola Pechenizkiy, Decebal Constantin Mocanu
2023	J	`jnl`	Automatic Noise Filtering with Dynamic Sparse Training in Deep Reinforcement Learning. CoRR Bram Grooten, Ghada Sokar, Shibhansh Dohare, Elena Mocanu, Matthew E. Taylor, Mykola Pechenizkiy, Decebal Constantin Mocanu
2023	A	`conf`	C AIED Calarina Muslimani, Saba Gul, Matthew E. Taylor, Carrie Demmans Epp, Christabel Wayllace
2023	A*	`conf`	Can You Improve My Code? Optimizing Programs with Local Search. IJCAI Fatemeh Abdollahi, Saqib Ameen, Matthew E. Taylor, Levi H. S. Lelis
2023	J	`jnl`	Can You Improve My Code? Optimizing Programs with Local Search. CoRR Fatemeh Abdollahi, Saqib Ameen, Matthew E. Taylor, Levi H. S. Lelis
2023	J	`jnl`	Curriculum Learning for Cooperation in Multi-Agent Reinforcement Learning. CoRR Rupali Bhati, Sai Krishna Gottipati, Clodéric Mars, Matthew E. Taylor
2023	A	`conf`	Do As You Teach: A Multi-Teacher Approach to Self-Play in Deep Reinforcement Learning. AAMAS Chaitanya Kharyal, Tanmay Kumar Sinha, Sai Krishna Gottipati, Fatemeh Abdollahi, Srijita Das, Matthew E. Taylor
2023	A	`conf`	Hiking up that HILL with Cogment-Verse: Train & Operate Multi-agent Systems Learning from Humans. AAMAS Sai Krishna Gottipati, Luong-Ha Nguyen, Clodéric Mars, Matthew E. Taylor
2023	J	`jnl`	Human-AI Collaboration in Real-World Complex Environment with Reinforcement Learning. CoRR Md. Saiful Islam, Srijita Das, Sai Krishna Gottipati, William Duguay, Clodéric Mars, Jalal Arabneydi, Antoine Fagette, Matthew Guzdial, Matthew E. Taylor
2023	J	`jnl`	Human-Machine Teaming for UAVs: An Experimentation Platform. CoRR Laila El Moujtahid, Sai Krishna Gottipati, Clodéric Mars, Matthew E. Taylor
2023	A*	`conf`	Ignorance is Bliss: Robust Control via Information Gating. NeurIPS Manan Tomar, Riashat Islam, Matthew E. Taylor, Sergey Levine, Philip Bachman
2023	J	`jnl`	Improving reinforcement learning with human assistance: an argument for human subject studies with HIPPO Gym. Neural Comput. Appl. Matthew E. Taylor, Nicholas Nissen, Yuan Wang, Neda Navidi
2023	C	`conf`	Innovating AI Leadership Education. FIE Xiaoxue Du, Sharifa Alghowinem, Matthew E. Taylor, Kate Darling, Cynthia Breazeal
2023	J	`jnl`	Learning Representations for Pixel-based Control: What Matters and Why? Trans. Mach. Learn. Res. Manan Tomar, Utkarsh A. Mishra, Amy Zhang, Matthew E. Taylor
2023	A	`conf`	Learning from Multiple Independent Advisors in Multi-agent Reinforcement Learning. AAMAS Sriram Ganapathi Subramanian, Matthew E. Taylor, Kate Larson, Mark Crowley
2023	J	`jnl`	Learning from Multiple Independent Advisors in Multi-agent Reinforcement Learning. CoRR Sriram Ganapathi Subramanian, Matthew E. Taylor, Kate Larson, Mark Crowley
2023	A*	`conf`	Learning to Shape Rewards Using a Game of Two Partners. AAAI 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
2023	J	`jnl`	MaDi: Learning to Mask Distractions for Generalization in Visual Deep Reinforcement Learning. CoRR Bram Grooten, Tristan Tomilin, Gautham Vasan, Matthew E. Taylor, A. Rupam Mahmood, Meng Fang, Mykola Pechenizkiy, Decebal Constantin Mocanu
2023	A*	`conf`	Model AI Assignments 2023. AAAI 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
2023	A*	`conf`	Multi-Agent Advisor Q-Learning (Extended Abstract). IJCAI Sriram Ganapathi Subramanian, Matthew E. Taylor, Kate Larson, Mark Crowley
2023	A	`conf`	Multi-Robot Warehouse Optimization: Leveraging Machine Learning for Improved Performance. AAMAS Mara Cairo, Bevin Eldaphonse, Payam Mousavi, Sahir, Sheikh Jubair, Matthew E. Taylor, Graham Doerksen, Nikolai Kummer, Jordan Maretzki, Gupreet Mohhar, Sean Murphy, Johannes Günther, Laura Petrich, Talat Syed
2023	A	`conf`	PORTAL: Automatic Curricula Generation for Multiagent Reinforcement Learning. AAMAS Jizhou Wu, Tianpei Yang, Xiaotian Hao, Jianye Hao, Yan Zheng, Weixun Wang, Matthew E. Taylor
2023	—	`conf`	RL-Ripper: : A Framework for Global Routing Using Reinforcement Learning and Smart Net Ripping Techniques. ACM Great Lakes Symposium on VLSI Upma Gandhi, Erfan Aghaeekiasaraee, Ismail S. K. Bustany, Payam Mousavi, Matthew E. Taylor, Laleh Behjat
2023	—	`conf`	Reinforcement Learning Requires Human-in-the-Loop Framing and Approaches. HHAI Matthew E. Taylor
2023	J	`jnl`	Reinforcement Teaching. Trans. Mach. Learn. Res. Calarina Muslimani, Alex Lewandowski, Dale Schuurmans, Matthew E. Taylor, Jun Luo
2023	A	`conf`	Two-Level Actor-Critic Using Multiple Teachers. AAMAS Su Zhang, Srijita Das, Sriram Ganapathi Subramanian, Matthew E. Taylor
2023	J	`jnl`	Two-Level Actor-Critic Using Multiple Teachers. Trans. Mach. Learn. Res. Su Zhang, Srijita Das, Sriram Ganapathi Subramanian, Matthew E. Taylor
2022	A	`ed.`	21st International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2022, Auckland, New Zealand, May 9-13, 2022 AAMAS Piotr Faliszewski, Viviana Mascardi, Catherine Pelachaud, Matthew E. Taylor
2022	J	`jnl`	Augmenting Flight Training with AI to Efficiently Train Pilots. CoRR Michael Guevarra, Srijita Das, Christabel Wayllace, Carrie Demmans Epp, Matthew E. Taylor, Alan Tay
2022	A	`conf`	Cross-domain adaptive transfer reinforcement learning based on state-action correspondence. UAI Heng You, Tianpei Yang, Yan Zheng, Jianye Hao, Matthew E. Taylor
2022	A*	`conf`	Decentralized Mean Field Games. AAAI Sriram Ganapathi Subramanian, Matthew E. Taylor, Mark Crowley, Pascal Poupart
2022	—	`conf`	Diversity for transfer in learning-based control of buildings. e-Energy Tianyu Zhang, Aakash Krishna G. S, Mohammad Afshari, Petr Musílek, Matthew E. Taylor, Omid Ardakanian
2022	J	`jnl`	Lucid dreaming for experience replay: refreshing past states with the current policy. Neural Comput. Appl. Yunshu Du, Garrett Warnell, Assefaw H. Gebremedhin, Peter Stone, Matthew E. Taylor
2022	J	`jnl`	Methodical Advice Collection and Reuse in Deep Reinforcement Learning. CoRR Sahir, Ercüment Ilhan, Srijita Das, Matthew E. Taylor
2022	J	`jnl`	Multi-Agent Advisor Q-Learning. J. Artif. Intell. Res. Sriram Ganapathi Subramanian, Matthew E. Taylor, Kate Larson, Mark Crowley
2022	A*	`conf`	Multiagent Q-learning with Sub-Team Coordination. NeurIPS Wenhan Huang, Kai Li, Kun Shao, Tianze Zhou, Matthew E. Taylor, Jun Luo, Dongge Wang, Hangyu Mao, Jianye Hao, Jun Wang, Xiaotie Deng
2022	J	`jnl`	NeurIPS 2022 Competition: Driving SMARTS. CoRR Amir Rasouli, Randy Goebel, Matthew E. Taylor, Iuliia Kotseruba, Soheil Alizadeh, Tianpei Yang, Montgomery Alban, Florian Shkurti, Yuzheng Zhuang, Adam Scibior, Kasra Rezaee, Animesh Garg, David Meger, Jun Luo, Liam Paull, Weinan Zhang, Xinyu Wang, Xi Chen
2022	A*	`conf`	PMIC: Improving Multi-Agent Reinforcement Learning with Progressive Mutual Information Collaboration. ICML Pengyi Li, Hongyao Tang, Tianpei Yang, Xiaotian Hao, Tong Sang, Yan Zheng, Jianye Hao, Matthew E. Taylor, Wenyuan Tao, Zhen Wang
2022	J	`jnl`	PMIC: Improving Multi-Agent Reinforcement Learning with Progressive Mutual Information Collaboration. CoRR Pengyi Li, Hongyao Tang, Tianpei Yang, Xiaotian Hao, Tong Sang, Yan Zheng, Jianye Hao, Matthew E. Taylor, Zhen Wang
2022	J	`jnl`	Policy invariant explicit shaping: an efficient alternative to reward shaping. Neural Comput. Appl. Paniz Behboudian, Yash Satsangi, Matthew E. Taylor, Anna Harutyunyan, Michael Bowling
2022	J	`jnl`	Reinforcement Teaching. CoRR Alex Lewandowski, Calarina Muslimani, Matthew E. Taylor, Jun Luo, Dale Schuurmans
2022	J	`jnl`	Safe Evaluation For Offline Learning: Are We Ready To Deploy? CoRR Hager Radi, Josiah P. Hanna, Peter Stone, Matthew E. Taylor
2022	J	`jnl`	Semi-Centralised Multi-Agent Reinforcement Learning with Policy-Embedded Training. CoRR Taher Jafferjee, Juliusz Krysztof Ziomek, Tianpei Yang, Zipeng Dai, Jianhong Wang, Matthew E. Taylor, Kun Shao, Jun Wang, David Mguni
2021	J	`jnl`	Decentralized Mean Field Games. CoRR Sriram Ganapathi Subramanian, Matthew E. Taylor, Mark Crowley, Pascal Poupart
2021	A	`conf`	Diverse Auto-Curriculum is Critical for Successful Real-World Multiagent Learning Systems. AAMAS Yaodong Yang, Jun Luo, Ying Wen, Oliver Slumbers, Daniel Graves, Haitham Bou-Ammar, Jun Wang, Matthew E. Taylor
2021	J	`jnl`	Diverse Auto-Curriculum is Critical for Successful Real-World Multiagent Learning Systems. CoRR Yaodong Yang, Jun Luo, Ying Wen, Oliver Slumbers, Daniel Graves, Haitham Bou-Ammar, Jun Wang, Matthew E. Taylor
2021	—	`conf`	Driving SMARTS Competition at NeurIPS 2022: Insights and Outcome. NeurIPS (Competition and Demos) Amir Rasouli, Soheil Alizadeh, Iuliia Kotseruba, Yi Ma, Hebin Liang, Yuan Tian, Zhiyu Huang, Haochen Liu, Jingda Wu, Randy Goebel, Tianpei Yang, Matthew E. Taylor, Liam Paull, Xi Chen
2021	J	`jnl`	HAMMER: Multi-Level Coordination of Reinforcement Learning Agents via Learned Messaging. CoRR Nikunj Gupta, G. Srinivasaraghavan, Swarup Kumar Mohalik, Matthew E. Taylor
2021	J	`jnl`	Improving Reinforcement Learning with Human Assistance: An Argument for Human Subject Studies with HIPPO Gym. CoRR Matthew E. Taylor, Nicholas Nissen, Yuan Wang, Neda Navidi
2021	J	`jnl`	Learning Representations for Pixel-based Control: What Matters and Why? CoRR Manan Tomar, Utkarsh A. Mishra, Amy Zhang, Matthew E. Taylor
2021	J	`jnl`	Model-Invariant State Abstractions for Model-Based Reinforcement Learning. CoRR Manan Tomar, Amy Zhang, Roberto Calandra, Matthew E. Taylor, Joelle Pineau
2021	J	`jnl`	Multi-Agent Advisor Q-Learning. CoRR Sriram Ganapathi Subramanian, Matthew E. Taylor, Kate Larson, Mark Crowley
2021	A	`conf`	Partially Observable Mean Field Reinforcement Learning. AAMAS Sriram Ganapathi Subramanian, Matthew E. Taylor, Mark Crowley, Pascal Poupart
2021	—	`conf`	Reinforcement Learning for Electronic Design Automation: Successes and Opportunities. ISPD Matthew E. Taylor
2021	J	`jnl`	The Atari Data Scraper. CoRR Brittany Davis Pierson, Justine Ventura, Matthew E. Taylor
2021	J	`jnl`	The Effect of Q-function Reuse on the Total Regret of Tabular, Model-Free, Reinforcement Learning. CoRR Volodymyr Tkachuk, Sriram Ganapathi Subramanian, Matthew E. Taylor
2021	A*	`conf`	Towered Actor Critic For Handling Multiple Action Types In Reinforcement Learning For Drug Discovery. AAAI Sai Krishna Gottipati, Yashaswi Pathak, Boris Sattarov, Sahir, Rohan Nuttall, Mohammad Amini, Matthew E. Taylor, Sarath Chandar
2020	J	`jnl`	A Conceptual Framework for Externally-influenced Agents: An Assisted Reinforcement Learning Review. CoRR Adam Bignold, Francisco Cruz, Matthew E. Taylor, Tim Brys, Richard Dazeley, Peter Vamplew, Cameron Foale
2020	A	`conf`	A Very Condensed Survey and Critique of Multiagent Deep Reinforcement Learning. AAMAS Pablo Hernandez-Leal, Bilal Kartal, Matthew E. Taylor
2020	J	`jnl`	Curriculum Learning for Reinforcement Learning Domains: A Framework and Survey. CoRR Sanmit Narvekar, Bei Peng, Matteo Leonetti, Jivko Sinapov, Matthew E. Taylor, Peter Stone
2020	J	`jnl`	Curriculum Learning for Reinforcement Learning Domains: A Framework and Survey. J. Mach. Learn. Res. Sanmit Narvekar, Bei Peng, Matteo Leonetti, Jivko Sinapov, Matthew E. Taylor, Peter Stone
2020	—	`ed.`	Distributed Artificial Intelligence - Second International Conference, DAI 2020, Nanjing, China, October 24-27, 2020, Proceedings DAI Matthew E. Taylor, Yang Yu, Edith Elkind, Yang Gao
2020	J	`jnl`	Effects of a computer-based learning environment that teaches older adults how to install a smart home system. Comput. Educ. Yang Hu, Rachel Min Wong, Olusola O. Adesope, Matthew E. Taylor
2020	J	`jnl`	Lucid Dreaming for Experience Replay: Refreshing Past States with the Current Policy. CoRR Yunshu Du, Garrett Warnell, Assefaw Hadish Gebremedhin, Peter Stone, Matthew E. Taylor
2020	J	`jnl`	Maximum Reward Formulation In Reinforcement Learning. CoRR Sai Krishna Gottipati, Yashaswi Pathak, Rohan Nuttall, Sahir, Raviteja Chunduru, Ahmed Touati, Sriram Ganapathi Subramanian, Matthew E. Taylor, Sarath Chandar
2020	A	`conf`	Multi Type Mean Field Reinforcement Learning. AAMAS Sriram Ganapathi Subramanian, Pascal Poupart, Matthew E. Taylor, Nidhi Hegde
2020	J	`jnl`	Multi Type Mean Field Reinforcement Learning. CoRR Sriram Ganapathi Subramanian, Pascal Poupart, Matthew E. Taylor, Nidhi Hegde
2020	J	`jnl`	Partially Observable Mean Field Reinforcement Learning. CoRR Sriram Ganapathi Subramanian, Matthew E. Taylor, Mark Crowley, Pascal Poupart
2020	A*	`conf`	Providing Uncertainty-Based Advice for Deep Reinforcement Learning Agents (Student Abstract). AAAI Felipe Leno da Silva, Pablo Hernandez-Leal, Bilal Kartal, Matthew E. Taylor
2020	J	`jnl`	Sequential Association Rule Mining for Autonomously Extracting Hierarchical Task Structures in Reinforcement Learning. IEEE Access Behzad Ghazanfari, Fatemeh Afghah, Matthew E. Taylor
2020	J	`jnl`	Study of Effectiveness of Prior Knowledge for Smart Home Kit Installation. Sensors Yang Hu, Diane J. Cook, Matthew E. Taylor
2020	A*	`conf`	Uncertainty-Aware Action Advising for Deep Reinforcement Learning Agents. AAAI Felipe Leno da Silva, Pablo Hernandez-Leal, Bilal Kartal, Matthew E. Taylor
2020	J	`jnl`	Useful Policy Invariant Shaping from Arbitrary Advice. CoRR Paniz Behboudian, Yash Satsangi, Matthew E. Taylor, Anna Harutyunyan, Michael Bowling
2020	J	`jnl`	Work in Progress: Temporally Extended Auxiliary Tasks. CoRR Craig Sherstan, Bilal Kartal, Pablo Hernandez-Leal, Matthew E. Taylor
2019	J	`jnl`	A survey and critique of multiagent deep reinforcement learning. Auton. Agents Multi Agent Syst. Pablo Hernandez-Leal, Bilal Kartal, Matthew E. Taylor
2019	—	`conf`	Achieving cooperation through deep multiagent reinforcement learning in sequential prisoner's dilemmas. DAI Weixun Wang, Jianye Hao, Yixi Wang, Matthew E. Taylor
2019	—	`conf`	Action Guidance with MCTS for Deep Reinforcement Learning. AIIDE Bilal Kartal, Pablo Hernandez-Leal, Matthew E. Taylor
2019	J	`jnl`	Action Guidance with MCTS for Deep Reinforcement Learning. CoRR Bilal Kartal, Pablo Hernandez-Leal, Matthew E. Taylor
2019	—	`conf`	Agent Modeling as Auxiliary Task for Deep Reinforcement Learning. AIIDE Pablo Hernandez-Leal, Bilal Kartal, Matthew E. Taylor
2019	J	`jnl`	Agent Modeling as Auxiliary Task for Deep Reinforcement Learning. CoRR Pablo Hernandez-Leal, Bilal Kartal, Matthew E. Taylor
2019	J	`jnl`	Analysis of University Fitness Center Data Uncovers Interesting Patterns, Enables Prediction. IEEE Trans. Knowl. Data Eng. Yunshu Du, Assefaw H. Gebremedhin, Matthew E. Taylor
2019	J	`jnl`	Interactive Learning of Environment Dynamics for Sequential Tasks. CoRR Robert T. Loftin, Bei Peng, Matthew E. Taylor, Michael L. Littman, David L. Roberts
2019	A*	`conf`	Interactive Reinforcement Learning with Dynamic Reuse of Prior Knowledge from Human and Agent Demonstrations. IJCAI Zhaodong Wang, Matthew E. Taylor
2019	J	`jnl`	Jointly Pre-training with Supervised, Autoencoder, and Value Losses for Deep Reinforcement Learning. CoRR Gabriel Victor de la Cruz, Yunshu Du, Matthew E. Taylor
2019	J	`jnl`	Learning to Teach Reinforcement Learning Agents. Mach. Learn. Knowl. Extr. Anestis Fachantidis, Matthew E. Taylor, Ioannis P. Vlahavas
2019	A*	`conf`	Metatrace Actor-Critic: Online Step-Size Tuning by Meta-gradient Descent for Reinforcement Learning Control. IJCAI Kenny Young, Baoxiang Wang, Matthew E. Taylor
2019	—	`conf`	On Hard Exploration for Reinforcement Learning: A Case Study in Pommerman. AIIDE Chao Gao, Bilal Kartal, Pablo Hernandez-Leal, Matthew E. Taylor
2019	J	`jnl`	On Hard Exploration for Reinforcement Learning: a Case Study in Pommerman. CoRR Chao Gao, Bilal Kartal, Pablo Hernandez-Leal, Matthew E. Taylor
2019	J	`jnl`	Pre-training with non-expert human demonstration for deep reinforcement learning. Knowl. Eng. Rev. Gabriel Victor de la Cruz, Yunshu Du, Matthew E. Taylor
2019	A	`ed.`	Proceedings of the 18th International Conference on Autonomous Agents and MultiAgent Systems, AAMAS '19, Montreal, QC, Canada, May 13-17, 2019 AAMAS Edith Elkind, Manuela Veloso, Noa Agmon, Matthew E. Taylor
2019	J	`jnl`	Robot-enabled support of daily activities in smart home environments. Cogn. Syst. Res. Garrett Wilson, Christopher Pereyda, Nisha Raghunath, Gabriel Victor de la Cruz, Shivam Goel, Sepehr Nesaei, Bryan David Minor, Maureen Schmitter-Edgecombe, Matthew E. Taylor, Diane J. Cook
2019	J	`jnl`	Safer Deep RL with Shallow MCTS: A Case Study in Pommerman. CoRR Bilal Kartal, Pablo Hernandez-Leal, Chao Gao, Matthew E. Taylor
2019	J	`jnl`	Skynet: A Top Deep RL Agent in the Inaugural Pommerman Team Competition. CoRR Chao Gao, Pablo Hernandez-Leal, Bilal Kartal, Matthew E. Taylor
2019	—	`conf`	Terminal Prediction as an Auxiliary Task for Deep Reinforcement Learning. AIIDE Bilal Kartal, Pablo Hernandez-Leal, Matthew E. Taylor
2019	J	`jnl`	Terminal Prediction as an Auxiliary Task for Deep Reinforcement Learning. CoRR Bilal Kartal, Pablo Hernandez-Leal, Matthew E. Taylor
2019	Misc	`conf`	Towers of Saliency: A Reinforcement Learning Visualization Using Immersive Environments. ISS Nathan Douglas, Dianna Yim, Bilal Kartal, Pablo Hernandez-Leal, Frank Maurer, Matthew E. Taylor
2018	J	`jnl`	Autonomous Extraction of a Hierarchical Structure of Tasks in Reinforcement Learning, A Sequential Associate Rule Mining Approach. CoRR Behzad Ghazanfari, Fatemeh Afghah, Matthew E. Taylor
2018	A*	`conf`	Autonomously Reusing Knowledge in Multiagent Reinforcement Learning. IJCAI Felipe Leno da Silva, Matthew E. Taylor, Anna Helena Reali Costa
2018	J	`jnl`	Curriculum Design for Machine Learners in Sequential Decision Tasks. IEEE Trans. Emerg. Top. Comput. Intell. Bei Peng, James MacGlashan, Robert Tyler Loftin, Michael L. Littman, David L. Roberts, Matthew E. Taylor
2018	A*	`conf`	Improving Reinforcement Learning with Human Input. IJCAI Matthew E. Taylor
2018	J	`jnl`	Interactive Reinforcement Learning with Dynamic Reuse of Prior Knowledge from Human/Agent's Demonstration. CoRR Zhaodong Wang, Matthew E. Taylor
2018	J	`jnl`	Is multiagent deep reinforcement learning the answer or the question? A brief survey. CoRR Pablo Hernandez-Leal, Bilal Kartal, Matthew E. Taylor
2018	J	`jnl`	Leveraging human knowledge in tabular reinforcement learning: A study of human subjects. CoRR Ariel Rosenfeld, Moshe Cohen, Matthew E. Taylor, Sarit Kraus
2018	J	`jnl`	Leveraging human knowledge in tabular reinforcement learning: a study of human subjects. Knowl. Eng. Rev. Ariel Rosenfeld, Moshe Cohen, Matthew E. Taylor, Sarit Kraus
2018	J	`jnl`	Metatrace: Online Step-size Tuning by Meta-gradient Descent for Reinforcement Learning Control. CoRR Kenny Young, Baoxiang Wang, Matthew E. Taylor
2018	J	`jnl`	Pre-training with Non-expert Human Demonstration for Deep Reinforcement Learning. CoRR Gabriel Victor de la Cruz, Yunshu Du, Matthew E. Taylor
2018	J	`jnl`	Towards Cooperation in Sequential Prisoner's Dilemmas: a Deep Multiagent Reinforcement Learning Approach. CoRR Weixun Wang, Jianye Hao, Yixi Wang, Matthew E. Taylor
2018	J	`jnl`	Using Monte Carlo Tree Search as a Demonstrator within Asynchronous Deep RL. CoRR Bilal Kartal, Pablo Hernandez-Leal, Matthew E. Taylor
2017	A*	`conf`	AI Projects for Computer Science Capstone Classes (Extended Abstract). AAAI Matthew E. Taylor, Sakire Arslan Ay
2017	A	`conf`	An Exploration Strategy Facing Non-Stationary Agents. AAMAS Pablo Hernandez-Leal, Yusen Zhan, Matthew E. Taylor, Luis Enrique Sucar, Enrique Munoz de Cote
2017	J	`jnl`	An exploration strategy for non-stationary opponents. Auton. Agents Multi Agent Syst. Pablo Hernandez-Leal, Yusen Zhan, Matthew E. Taylor, Luis Enrique Sucar, Enrique Munoz de Cote
2017	J	`jnl`	Autonomous Extracting a Hierarchical Structure of Tasks in Reinforcement Learning and Multi-task Reinforcement Learning. CoRR Behzad Ghazanfari, Matthew E. Taylor
2017	J	`jnl`	Bin-Dog: A Robotic Platform for Bin Management in Orchards. Robotics Yunxiang Ye, Zhaodong Wang, Dylan Jones, Long He, Matthew E. Taylor, Geoffrey A. Hollinger, Qin Zhang
2017	A	`conf`	Curriculum Design for Machine Learners in Sequential Decision Tasks. AAMAS Bei Peng, James MacGlashan, Robert T. Loftin, Michael L. Littman, David L. Roberts, Matthew E. Taylor
2017	A	`conf`	Detecting Switches Against Non-Stationary Opponents. AAMAS Pablo Hernandez-Leal, Yusen Zhan, Matthew E. Taylor, Luis Enrique Sucar, Enrique Munoz de Cote
2017	J	`jnl`	Efficiently detecting switches against non-stationary opponents. Auton. Agents Multi Agent Syst. Pablo Hernandez-Leal, Yusen Zhan, Matthew E. Taylor, Luis Enrique Sucar, Enrique Munoz de Cote
2017	A*	`conf`	Improving Reinforcement Learning with Confidence-Based Demonstrations. IJCAI Zhaodong Wang, Matthew E. Taylor
2017	A*	`conf`	Interactive Learning from Policy-Dependent Human Feedback. ICML James MacGlashan, Mark K. Ho, Robert Tyler Loftin, Bei Peng, Guan Wang, David L. Roberts, Matthew E. Taylor, Michael L. Littman
2017	J	`jnl`	Interactive Learning from Policy-Dependent Human Feedback. CoRR James MacGlashan, Mark K. Ho, Robert Tyler Loftin, Bei Peng, David L. Roberts, Matthew E. Taylor, Michael L. Littman
2017	J	`jnl`	Learning to Teach Reinforcement Learning Agents. CoRR Anestis Fachantidis, Matthew E. Taylor, Ioannis P. Vlahavas
2017	A*	`conf`	Leveraging Human Knowledge in Tabular Reinforcement Learning: A Study of Human Subjects. IJCAI Ariel Rosenfeld, Matthew E. Taylor, Sarit Kraus
2017	J	`jnl`	Multi-objectivization and ensembles of shapings in reinforcement learning. Neurocomputing Tim Brys, Anna Harutyunyan, Peter Vrancx, Ann Nowé, Matthew E. Taylor
2017	J	`jnl`	Nonconvex Policy Search Using Variational Inequalities. Neural Comput. Yusen Zhan, Haitham Bou-Ammar, Matthew E. Taylor
2017	J	`jnl`	Pre-training Neural Networks with Human Demonstrations for Deep Reinforcement Learning. CoRR Gabriel Victor de la Cruz, Yunshu Du, Matthew E. Taylor
2017	A*	`conf`	Scalable Multitask Policy Gradient Reinforcement Learning. AAAI Salam El Bsat, Haitham Bou-Ammar, Matthew E. Taylor
2017	J	`jnl`	Scalable lifelong reinforcement learning. Pattern Recognit. Yusen Zhan, Haitham Bou-Ammar, Matthew E. Taylor
2017	—	`conf`	Solar Decathlon Competition: Towards a Solar-Powered Smart Home. AAAI Workshops Amanda Leah Zulas, Kaitlyn I. Franz, Darrin Griechen, Matthew E. Taylor
2017	A	`conf`	Speeding up Tabular Reinforcement Learning Using State-Action Similarities. AAMAS Ariel Rosenfeld, Matthew E. Taylor, Sarit Kraus
2016	A	`conf`	A Bayesian Approach for Learning and Tracking Switching, Non-Stationary Opponents: (Extended Abstract). AAMAS Pablo Hernandez-Leal, Benjamin Rosman, Matthew E. Taylor, Luis Enrique Sucar, Enrique Munoz de Cote
2016	A	`conf`	A Need for Speed: Adapting Agent Action Speed to Improve Task Learning from Non-Expert Humans. AAMAS Bei Peng, James MacGlashan, Robert Tyler Loftin, Michael L. Littman, David L. Roberts, Matthew E. Taylor
2016	—	`conf`	Dimensionality Reduced Reinforcement Learning for Assistive Robots. AAAI Fall Symposia William Curran, Tim Brys, David W. Aha, Matthew E. Taylor, William D. Smart
2016	—	`conf`	Effective Transfer via Demonstrations in Reinforcement Learning: A Preliminary Study. AAAI Spring Symposia Zhaodong Wang, Matthew E. Taylor
2016	—	`conf`	Identifying and Tracking Switching, Non-Stationary Opponents: A Bayesian Approach. AAAI Workshop: Multiagent Interaction without Prior Coordination Pablo Hernandez-Leal, Matthew E. Taylor, Benjamin Rosman, Luis Enrique Sucar, Enrique Munoz de Cote
2016	J	`jnl`	Learning behaviors via human-delivered discrete feedback: modeling implicit feedback strategies to speed up learning. Auton. Agents Multi Agent Syst. Robert T. Loftin, Bei Peng, James MacGlashan, Michael L. Littman, Matthew E. Taylor, Jeff Huang, David L. Roberts
2016	A	`conf`	Learning from Demonstration for Shaping through Inverse Reinforcement Learning. AAMAS Halit Bener Suay, Tim Brys, Matthew E. Taylor, Sonia Chernova
2016	A	`conf`	Lifelong learning for disturbance rejection on mobile robots. IROS David Isele, José-Marcio Luna, Eric Eaton, Gabriel Victor de la Cruz, James Irwin, Brandon Kallaher, Matthew E. Taylor
2016	A*	`conf`	Theoretically-Grounded Policy Advice from Multiple Teachers in Reinforcement Learning Settings with Applications to Negative Transfer. IJCAI Yusen Zhan, Haitham Bou-Ammar, Matthew E. Taylor
2016	J	`jnl`	Theoretically-Grounded Policy Advice from Multiple Teachers in Reinforcement Learning Settings with Applications to Negative Transfer. CoRR Yusen Zhan, Haitham Bou-Ammar, Matthew E. Taylor
2016	—	`conf`	Towards Behavior-Aware Model Learning from Human-Generated Trajectories. AAAI Fall Symposia Robert Tyler Loftin, James MacGlashan, Bei Peng, Matthew E. Taylor, Michael L. Littman, David L. Roberts
2015	A	`conf`	Bidding in Non-Stationary Energy Markets. AAMAS Pablo Hernandez-Leal, Matthew E. Taylor, Enrique Munoz de Cote, Luis Enrique Sucar
2015	—	`conf`	On the Ability to Provide Demonstrations on a UAS: Observing 90 Untrained Participants Abusing a Flying Robot. AAAI Fall Symposia Mitchell Scott, Bei Peng, Madeline Chili, Tanay Nigam, Francis G. Pascual, Cynthia Matuszek, Matthew E. Taylor
2015	—	`conf`	Online Transfer Learning in Reinforcement Learning Domains. AAAI Fall Symposia Yusen Zhan, Matthew E. Taylor
2015	J	`jnl`	Online Transfer Learning in Reinforcement Learning Domains. CoRR Yusen Zhan, Matthew E. Taylor
2015	A	`conf`	Policy Transfer using Reward Shaping. AAMAS Tim Brys, Anna Harutyunyan, Matthew E. Taylor, Ann Nowé
2015	A*	`conf`	Reinforcement Learning from Demonstration through Shaping. IJCAI Tim Brys, Anna Harutyunyan, Halit Bener Suay, Sonia Chernova, Matthew E. Taylor, Ann Nowé
2015	—	`conf`	Towards Integrating Real-Time Crowd Advice with Reinforcement Learning. IUI Companion Gabriel Victor de la Cruz, Bei Peng, Walter S. Lasecki, Matthew E. Taylor
2015	J	`jnl`	Transfer learning with probabilistic mapping selection. Adapt. Behav. Anestis Fachantidis, Ioannis Partalas, Matthew E. Taylor, Ioannis P. Vlahavas
2015	A*	`conf`	Unsupervised Cross-Domain Transfer in Policy Gradient Reinforcement Learning via Manifold Alignment. AAAI Haitham Bou-Ammar, Eric Eaton, Paul Ruvolo, Matthew E. Taylor
2015	J	`jnl`	Using PCA to Efficiently Represent State Spaces. CoRR William Curran, Tim Brys, Matthew E. Taylor, William D. Smart
2014	A*	`conf`	A Strategy-Aware Technique for Learning Behaviors from Discrete Human Feedback. AAAI Robert Tyler Loftin, James MacGlashan, Bei Peng, Matthew E. Taylor, Michael L. Littman, Jeff Huang, David L. Roberts
2014	A	`conf`	Adaptive objective selection for correlated objectives in multi-objective reinforcement learning. AAMAS Tim Brys, Kristof Van Moffaert, Ann Nowé, Matthew E. Taylor
2014	—	`conf`	Agents Teaching Agents in Reinforcement Learning (Nectar Abstract). ECML/PKDD (3) Matthew E. Taylor, Lisa Torrey
2014	—	`conf`	An Autonomous Transfer Learning Algorithm for TD-Learners. SETN Anestis Fachantidis, Ioannis Partalas, Matthew E. Taylor, Ioannis P. Vlahavas
2014	—	`conf`	CLEAN Rewards to Improve Coordination by Removing Exploratory Action Noise. WI-IAT (3) Chris HolmesParker, Matthew E. Taylor, Adrian K. Agogino, Kagan Tumer
2014	A	`conf`	CLEANing the reward: counterfactual actions to remove exploratory action noise in multiagent learning (extended abstract). AAMAS Chris HolmesParker, Matthew E. Taylor, Adrian K. Agogino, Kagan Tumer
2014	A*	`conf`	Combining Multiple Correlated Reward and Shaping Signals by Measuring Confidence. AAAI Tim Brys, Ann Nowé, Daniel Kudenko, Matthew E. Taylor
2014	J	`jnl`	Distributed learning and multi-objectivity in traffic light control. Connect. Sci. Tim Brys, Tong T. Pham, Matthew E. Taylor
2014	B	`conf`	Learning something from nothing: Leveraging implicit human feedback strategies. RO-MAN Robert Tyler Loftin, Bei Peng, James MacGlashan, Michael L. Littman, Matthew E. Taylor, Jeff Huang, David L. Roberts
2014	B	`conf`	Multi-objectivization of reinforcement learning problems by reward shaping. IJCNN Tim Brys, Anna Harutyunyan, Peter Vrancx, Matthew E. Taylor, Daniel Kudenko, Ann Nowé
2014	A*	`conf`	Online Multi-Task Learning for Policy Gradient Methods. ICML Haitham Bou-Ammar, Eric Eaton, Paul Ruvolo, Matthew E. Taylor
2014	J	`jnl`	Reinforcement learning agents providing advice in complex video games. Connect. Sci. Matthew E. Taylor, Nicholas Carboni, Anestis Fachantidis, Ioannis P. Vlahavas, Lisa Torrey
2014	A	`conf`	Using Ensemble Techniques and Multi-Objectivization to Solve Reinforcement Learning Problems. ECAI Tim Brys, Matthew E. Taylor, Ann Nowé
2013	—	`conf`	Automatically Mapped Transfer between Reinforcement Learning Tasks via Three-Way Restricted Boltzmann Machines. ECML/PKDD (2) Haitham Bou-Ammar, Decebal Constantin Mocanu, Matthew E. Taylor, Kurt Driessens, Karl Tuyls, Gerhard Weiss
2013	—	`conf`	Autonomous Selection of Inter-Task Mappings in Transfer Learning (extended abstract). AAAI Spring Symposium: Lifelong Machine Learning Anestis Fachantidis, Ioannis Partalas, Matthew E. Taylor, Ioannis P. Vlahavas
2013	—	`conf`	Learning for Mobile-Robot Error Recovery (Extended Abstract). AAAI Spring Symposium: Designing Intelligent Robots Ravi Balasubramanian, Matthew E. Taylor
2013	J	`jnl`	Mitigating multi-path fading in a mobile mesh network. Ad Hoc Networks Marcos Augusto M. Vieira, Matthew E. Taylor, Prateek Tandon, Manish Jain, Ramesh Govindan, Gaurav S. Sukhatme, Milind Tambe
2013	A	`conf`	Teaching on a budget: agents advising agents in reinforcement learning. AAMAS Lisa Torrey, Matthew E. Taylor
2012	A	`conf`	Reinforcement learning transfer via sparse coding. AAMAS Haitham Bou-Ammar, Karl Tuyls, Matthew E. Taylor, Kurt Driessens, Gerhard Weiss
2012	A	`conf`	Towards student/teacher learning in sequential decision tasks. AAMAS Lisa Torrey, Matthew E. Taylor
2011	J	`jnl`	An Introduction to Intertask Transfer for Reinforcement Learning. AI Mag. Matthew E. Taylor, Peter Stone
2011	J	`jnl`	Distributed on-Line Multi-Agent Optimization under Uncertainty: Balancing Exploration and Exploitation. Adv. Complex Syst. Matthew E. Taylor, Manish Jain, Prateek Tandon, Makoto Yokoo, Milind Tambe
2011	A	`conf`	ESCAPES: evacuation simulation with children, authorities, parents, emotions, and social comparison. AAMAS Jason Tsai, Natalie Fridman, Emma Bowring, Matthew Brown, Shira Epstein, Gal A. Kaminka, Stacy Marsella, Andrew Ogden, Inbal Rika, Ankur Sheel, Matthew E. Taylor, Xuezhi Wang, Avishay Zilka, Milind Tambe
2011	J	`jnl`	Editorial: Agents and Multi-Agent Systems. Adv. Complex Syst. Frank Schweitzer, Matthew E. Taylor
2011	—	`conf`	Flood Disaster Mitigation: A Real-World Challenge Problem for Multi-agent Unmanned Surface Vehicles. AAMAS Workshops Paul Scerri, Balajee Kannan, Prasanna Velagapudi, Kate Macarthur, Peter Stone, Matthew E. Taylor, John M. Dolan, Alessandro Farinelli, Archie C. Chapman, Bernadine Dias, George Kantor
2011	A	`conf`	Integrating reinforcement learning with human demonstrations of varying ability. AAMAS Matthew E. Taylor, Halit Bener Suay, Sonia Chernova
2011	A	`conf`	Metric learning for reinforcement learning agents. AAMAS Matthew E. Taylor, Brian Kulis, Fei Sha
2011	—	`conf`	Model AI Assignments 2011. EAAI Todd W. Neller, Marie desJardins, Tim Oates, Matthew E. Taylor
2011	—	`conf`	Protecting against evaluation overfitting in empirical reinforcement learning. ADPRL Shimon Whiteson, Brian Tanner, Matthew E. Taylor, Peter Stone
2011	C	`conf`	Reinforcement Learning Transfer Using a Sparse Coded Inter-task Mapping. EUMAS Haitham Bou-Ammar, Matthew E. Taylor, Karl Tuyls, Gerhard Weiss
2011	—	`conf`	Reinforcement Learning Transfer via Common Subspaces. ALA Haitham Bou-Ammar, Matthew E. Taylor
2011	A	`conf`	Teamwork in distributed POMDPs: execution-time coordination under model uncertainty. AAMAS Jun-young Kwak, Rong Yang, Zhengyu Yin, Matthew E. Taylor, Milind Tambe
2011	—	`conf`	Towards Addressing Model Uncertainty: Robust Execution-Time Coordination for Teamwork. IAT Jun-young Kwak, Rong Yang, Zhengyu Yin, Matthew E. Taylor, Milind Tambe
2011	—	`conf`	Transfer Learning via Multiple Inter-task Mappings. EWRL Anestis Fachantidis, Ioannis Partalas, Matthew E. Taylor, Ioannis P. Vlahavas
2010	J	`jnl`	A Framework for Evaluating Deployed Security Systems: Is There a Chink in your ARMOR? Informatica (Slovenia) Matthew E. Taylor, Christopher Kiekintveld, Craig Western, Milind Tambe
2010	—	`ed.`	Adaptive and Learning Agents, Second Workshop, ALA 2009, Held as Part of the AAMAS 2009 Conference in Budapest, Hungary, May 12, 2009, Revised Selected Papers ALA Matthew E. Taylor, Karl Tuyls
2010	J	`jnl`	Critical factors in the empirical performance of temporal difference and evolutionary methods for reinforcement learning. Auton. Agents Multi Agent Syst. Shimon Whiteson, Matthew E. Taylor, Peter Stone
2010	A*	`conf`	Evolving Compiler Heuristics to Manage Communication and Contention. AAAI Matthew E. Taylor, Katherine E. Coons, Behnam Robatmili, Bertrand A. Maher, Doug Burger, Kathryn S. McKinley
2010	—	`conf`	Teamwork and Coordination under Model Uncertainty in DEC-POMDPs. Interactive Decision Theory and Game Theory Jun-young Kwak, Rong Yang, Zhengyu Yin, Matthew E. Taylor, Milind Tambe
2010	A	`conf`	When should there be a "Me" in "Team"?: distributed multi-agent optimization under uncertainty. AAMAS Matthew E. Taylor, Manish Jain, Yanquin Jin, Makoto Yokoo, Milind Tambe
2009	J	`jnl`	AAAI 2008 Workshop Reports. AI Mag. Razvan C. Bunescu, Vitor R. Carvalho, Jan Chomicki, Vincent Conitzer, Michael T. Cox, Virginia Dignum, Zachary Dodds, Mark Dredze, David Furcy, Evgeniy Gabrilovich, Mehmet H. Göker, Hans W. Guesgen, Haym Hirsh, Dietmar Jannach, Ulrich Junker, Wolfgang Ketter, Alfred Kobsa, Sven Koenig, Tessa A. Lau, Lundy Lewis, Eric T. Matson, Ted Metzler, Rada Mihalcea, Bamshad Mobasher, Joelle Pineau, Pascal Poupart, Anita Raja, Wheeler Ruml, Norman M. Sadeh, Guy Shani, Daniel G. Shapiro, Sarabjot Singh Anand, Matthew E. Taylor, Kiri Wagstaff, Trey Smith, William E. Walsh, Rong Zhou
2009	—	`conf`	Abstraction and Generalization in Reinforcement Learning: A Summary and Framework. ALA Marc J. V. Ponsen, Matthew E. Taylor, Karl Tuyls
2009	—	`conf`	Assisting Transfer-Enabled Machine Learning Algorithms: Leveraging Human Knowledge for Curriculum Design. AAAI Spring Symposium: Agents that Learn from Human Teachers Matthew E. Taylor
2009	A*	`conf`	DCOPs Meet the Real World: Exploring Unknown Reward Matrices with Applications to Mobile Sensor Networks. IJCAI Manish Jain, Matthew E. Taylor, Milind Tambe, Makoto Yokoo
2009	A*	`conf`	Exploiting Coordination Locales in Distributed POMDPs via Social Model Shaping. ICAPS Pradeep Varakantham, Jun-young Kwak, Matthew E. Taylor, Janusz Marecki, Paul Scerri, Milind Tambe
2009	J	`jnl`	Transfer Learning for Reinforcement Learning Domains: A Survey. J. Mach. Learn. Res. Matthew E. Taylor, Peter Stone
2009	—	`book`	Transfer in Reinforcement Learning Domains Matthew E. Taylor
2008	—	`conf`	Autonomous transfer for reinforcement learning. AAMAS (1) Matthew E. Taylor, Gregory Kuhlmann, Peter Stone
2008	B	`conf`	Feature selection and policy optimization for distributed instruction placement using reinforcement learning. PACT Katherine E. Coons, Behnam Robatmili, Matthew E. Taylor, Bertrand A. Maher, Doug Burger, Kathryn S. McKinley
2008	—	`conf`	Transfer Learning and Intelligence: an Argument and Approach. AGI Matthew E. Taylor, Gregory Kuhlmann, Peter Stone
2008	—	`conf`	Transferring Instances for Model-Based Reinforcement Learning. ECML/PKDD (2) Matthew E. Taylor, Nicholas K. Jong, Peter Stone
2007	Misc	`conf`	Autonomous Classification of Knowledge into an Ontology. FLAIRS Matthew E. Taylor, Cynthia Matuszek, Bryan Klimt, Michael Witbrock
2007	A*	`conf`	Autonomous Inter-Task Transfer in Reinforcement Learning Domains. AAAI Matthew E. Taylor
2007	A*	`conf`	Cross-domain transfer for reinforcement learning. ICML Matthew E. Taylor, Peter Stone
2007	J	`jnl`	Empirical Studies in Action Selection with Reinforcement Learning. Adapt. Behav. Shimon Whiteson, Matthew E. Taylor, Peter Stone
2007	Misc	`conf`	Guiding Inference with Policy Search Reinforcement Learning. FLAIRS Matthew E. Taylor, Cynthia Matuszek, Pace Reagan Smith, Michael Witbrock
2007	A	`conf`	IFSA: incremental feature-set augmentation for reinforcement learning tasks. AAMAS Mazda Ahmadi, Matthew E. Taylor, Peter Stone
2007	—	`conf`	Representation Transfer for Reinforcement Learning. AAAI Fall Symposium: Computational Approaches to Representation Change during Learning and Development Matthew E. Taylor, Peter Stone
2007	A*	`conf`	Representation Transfer via Elaboration. AAAI Matthew E. Taylor, Peter Stone
2007	A*	`conf`	Temporal Difference and Policy Search Methods for Reinforcement Learning: An Empirical Comparison. AAAI Matthew E. Taylor, Shimon Whiteson, Peter Stone
2007	A	`conf`	Towards reinforcement learning representation transfer. AAMAS Matthew E. Taylor, Peter Stone
2007	J	`jnl`	Transfer Learning via Inter-Task Mappings for Temporal Difference Learning. J. Mach. Learn. Res. Matthew E. Taylor, Peter Stone, Yaxin Liu
2007	A	`conf`	Transfer via inter-task mappings in policy search reinforcement learning. AAMAS Matthew E. Taylor, Shimon Whiteson, Peter Stone
2006	A	`conf`	Comparing evolutionary and temporal difference methods in a reinforcement learning domain. GECCO Matthew E. Taylor, Shimon Whiteson, Peter Stone
2006	A*	`conf`	Inter-Task Action Correlation for Reinforcement Learning Tasks. AAAI Matthew E. Taylor, Peter Stone
2005	A	`conf`	Behavior transfer for value-function-based reinforcement learning. AAMAS Matthew E. Taylor, Peter Stone
2005	C	`conf`	Keepaway Soccer: From Machine Learning Testbed to Benchmark. RoboCup Peter Stone, Gregory Kuhlmann, Matthew E. Taylor, Yaxin Liu
2005	A*	`conf`	Value Functions for RL-Based Behavior Transfer: A Comparative Study. AAAI Matthew E. Taylor, Peter Stone, Yaxin Liu