Matthew E. Taylor

ORCID

Publication Activity (10 Years)

Years Active: 2005-2024
Publications (10 Years): 155

Top Venues

Publications

Brittany Davis Pierson, Dustin Arendt, John Miller, Matthew E. Taylor
Comparing explanations in RL. Neural Comput. Appl. 36 (1) (2024)
Shang Wang, Deepak Ranganatha Sastry Mamillapalli, Tianpei Yang, Matthew E. Taylor
FPGA Divide-and-Conquer Placement using Deep Reinforcement Learning. CoRR (2024)
Simone Parisi, Montaser Mohammedalamen, Alireza Kazemipour, Matthew E. Taylor, Michael Bowling
Monitored Markov Decision Processes. CoRR (2024)
Chaitanya Kharyal, Sai Krishna Gottipati, Tanmay Kumar Sinha, Srijita Das, Matthew E. Taylor
GLIDE-RL: Grounded Language Instruction through DEmonstration in RL. CoRR (2024)
Simone Parisi, Montaser Mohammedalamen, Alireza Kazemipour, Matthew E. Taylor, Michael Bowling
Monitored Markov Decision Processes. AAMAS (2024)
Atefeh Shahroudnejad, Payam Mousavi, Oleksii Perepelytsia, Sahir, David Staszak, Matthew E. Taylor, Brent Bawel
A Novel Framework for Automated Warehouse Layout Generation. CoRR (2024)
Tianpei Yang, Heng You, Jianye Hao, Yan Zheng, Matthew E. Taylor
A Transfer Approach Using Graph Neural Networks in Deep Reinforcement Learning. AAAI (2024)
Carl Orge Retzlaff, Srijita Das, Christabel Wayllace, Payam Mousavi, Mohammad Afshari, Tianpei Yang, Anna Saranti, Alessa Angerschmid, Matthew E. Taylor, Andreas Holzinger
Human-in-the-Loop Reinforcement Learning: A Survey and Position on Requirements, Challenges, and Opportunities. J. Artif. Intell. Res. 79 (2024)
Chaitanya Kharyal, Sai Krishna Gottipati, Tanmay Kumar Sinha, Srijita Das, Matthew E. Taylor
GLIDE-RL: Grounded Language Instruction through DEmonstration in RL. AAMAS (2024)
Matan Shamir, Osher Elhadad, Matthew E. Taylor, Reuth Mirsky
ODGR: Online Dynamic Goal Recognition. CoRR (2024)
Calarina Muslimani, Matthew E. Taylor
Leveraging Sub-Optimal Data for Human-in-the-Loop Reinforcement Learning. AAMAS (2024)
Raechel Walker, Olivia Dias, Matthew E. Taylor, Cynthia Breazeal
Alleviating the Danger Of A Single Story Through Liberatory Computing Education. RESPECT (2024)
Bram Grooten, Tristan Tomilin, Gautham Vasan, Matthew E. Taylor, A. Rupam Mahmood, Meng Fang, Mykola Pechenizkiy, Decebal Constantin Mocanu
MaDi: Learning to Mask Distractions for Generalization in Visual Deep Reinforcement Learning. AAMAS (2024)
Jizhou Wu, Jianye Hao, Tianpei Yang, Xiaotian Hao, Yan Zheng, Weixun Wang, Matthew E. Taylor
PORTAL: Automatic Curricula Generation for Multiagent Reinforcement Learning. AAAI (2024)
Manan Tomar, Philippe Hansen-Estruch, Philip Bachman, Alex Lamb, John Langford, Matthew E. Taylor, Sergey Levine
Video Occupancy Models. CoRR (2024)
Calarina Muslimani, Matthew E. Taylor
Leveraging Sub-Optimal Data for Human-in-the-Loop Reinforcement Learning. CoRR (2024)
Qianxi Li, Yingyue Cao, Jikun Kang, Tianpei Yang, Xi Chen, Jun Jin, Matthew E. Taylor
LaFFi: Leveraging Hybrid Natural Language Feedback for Fine-tuning Language Models. CoRR (2024)
Calarina Muslimani, Bram Grooten, Deepak Ranganatha Sastry Mamillapalli, Mykola Pechenizkiy, Decebal Constantin Mocanu, Matthew E. Taylor
Boosting Robustness in Preference-Based Reinforcement Learning with Dynamic Sparsity. CoRR (2024)
Hao Zhang, Tianpei Yang, Yan Zheng, Jianye Hao, Matthew E. Taylor
PADDLE: Logic Program Guided Policy Reuse in Deep Reinforcement Learning. AAMAS (2024)
Afia Abedin, Abdul Bais, Cody Buntain, Laura Courchesne, Brian McQuinn, Matthew E. Taylor, Muhib Ullah
A Call to Arms: AI Should be Critical for Social Media Analysis of Conflict Zones. CoRR (2023)
Fatemeh Abdollahi, Saqib Ameen, Matthew E. Taylor, Levi H. S. Lelis
Can You Improve My Code? Optimizing Programs with Local Search. IJCAI (2023)
Michael Guevarra, Srijita Das, Christabel Wayllace, Carrie Demmans Epp, Matthew E. Taylor, Alan Tay
Augmenting Flight Training with AI to Efficiently Train Pilots. AAAI (2023)
Manan Tomar, Utkarsh A. Mishra, Amy Zhang, Matthew E. Taylor
Learning Representations for Pixel-based Control: What Matters and Why? Trans. Mach. Learn. Res. 2023 (2023)
Matthew E. Taylor, Nicholas Nissen, Yuan Wang, Neda Navidi
Improving reinforcement learning with human assistance: an argument for human subject studies with HIPPO Gym. Neural Comput. Appl. 35 (32) (2023)
Upma Gandhi, Erfan Aghaeekiasaraee, Ismail S. K. Bustany, Payam Mousavi, Matthew E. Taylor, Laleh Behjat
RL-Ripper: : A Framework for Global Routing Using Reinforcement Learning and Smart Net Ripping Techniques. ACM Great Lakes Symposium on VLSI (2023)
Sai Krishna Gottipati, Luong-Ha Nguyen, Clodéric Mars, Matthew E. Taylor
Hiking up that HILL with Cogment-Verse: Train & Operate Multi-agent Systems Learning from Humans. AAMAS (2023)
Sriram Ganapathi Subramanian, Matthew E. Taylor, Kate Larson, Mark Crowley
Learning from Multiple Independent Advisors in Multi-agent Reinforcement Learning. CoRR (2023)
Matthew E. Taylor
Reinforcement Learning Requires Human-in-the-Loop Framing and Approaches. HHAI (2023)
Adam Bignold, Francisco Cruz, Matthew E. Taylor, Tim Brys, Richard Dazeley, Peter Vamplew, Cameron Foale
A conceptual framework for externally-influenced agents: an assisted reinforcement learning review. J. Ambient Intell. Humaniz. Comput. 14 (4) (2023)
Laila El Moujtahid, Sai Krishna Gottipati, Clodéric Mars, Matthew E. Taylor
Human-Machine Teaming for UAVs: An Experimentation Platform. CoRR (2023)
Fatemeh Abdollahi, Saqib Ameen, Matthew E. Taylor, Levi H. S. Lelis
Can You Improve My Code? Optimizing Programs with Local Search. CoRR (2023)
Calarina Muslimani, Saba Gul, Matthew E. Taylor, Carrie Demmans Epp, Christabel Wayllace
Tutor: Helping People Learn to Avoid Present Bias During Decision Making. AIED (2023)
Md Saiful Islam, Srijita Das, Sai Krishna Gottipati, William Duguay, Clodéric Mars, Jalal Arabneydi, Antoine Fagette, Matthew Guzdial, Matthew E. Taylor
Human-AI Collaboration in Real-World Complex Environment with Reinforcement Learning. CoRR (2023)
Calarina Muslimani, Alex Lewandowski, Dale Schuurmans, Matthew E. Taylor, Jun Luo
Reinforcement Teaching. Trans. Mach. Learn. Res. 2023 (2023)
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
ASN: action semantics network for multiagent reinforcement learning. Auton. Agents Multi Agent Syst. 37 (2) (2023)
Bram Grooten, Ghada Sokar, Shibhansh Dohare, Elena Mocanu, Matthew E. Taylor, Mykola Pechenizkiy, Decebal Constantin Mocanu
Automatic Noise Filtering with Dynamic Sparse Training in Deep Reinforcement Learning. CoRR (2023)
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
Learning to Shape Rewards Using a Game of Two Partners. AAAI (2023)
Bram Grooten, Ghada Sokar, Shibhansh Dohare, Elena Mocanu, Matthew E. Taylor, Mykola Pechenizkiy, Decebal Constantin Mocanu
Automatic Noise Filtering with Dynamic Sparse Training in Deep Reinforcement Learning. AAMAS (2023)
Mara Cairo, Bevin Eldaphonse, Payam Mousavi, Sahir, Sheikh Jubair, Matthew E. Taylor, Graham Doerksen, Nikolai Kummer, Jordan Maretzki, Gupreet Mohhar, Sean Murphy, Johannes Gunther, Laura Petrich, Talat Syed
Multi-Robot Warehouse Optimization: Leveraging Machine Learning for Improved Performance. AAMAS (2023)
Chaitanya Kharyal, Tanmay Sinha, Sai Krishna Gottipati, Fatemeh Abdollahi, Srijita Das, Matthew E. Taylor
Do As You Teach: A Multi-Teacher Approach to Self-Play in Deep Reinforcement Learning. AAMAS (2023)
Sriram Ganapathi Subramanian, Matthew E. Taylor, Kate Larson, Mark Crowley
Learning from Multiple Independent Advisors in Multi-agent Reinforcement Learning. AAMAS (2023)
Sriram Ganapathi Subramanian, Matthew E. Taylor, Kate Larson, Mark Crowley
Multi-Agent Advisor Q-Learning (Extended Abstract). IJCAI (2023)
Rupali Bhati, Sai Krishna Gottipati, Clodéric Mars, Matthew E. Taylor
Curriculum Learning for Cooperation in Multi-Agent Reinforcement Learning. CoRR (2023)
Su Zhang, Srijita Das, Sriram Ganapathi Subramanian, Matthew E. Taylor
Two-Level Actor-Critic Using Multiple Teachers. Trans. Mach. Learn. Res. 2023 (2023)
Bram Grooten, Tristan Tomilin, Gautham Vasan, Matthew E. Taylor, A. Rupam Mahmood, Meng Fang, Mykola Pechenizkiy, Decebal Constantin Mocanu
MaDi: Learning to Mask Distractions for Generalization in Visual Deep Reinforcement Learning. CoRR (2023)
Su Zhang, Srijita Das, Sriram Ganapathi Subramanian, Matthew E. Taylor
Two-Level Actor-Critic Using Multiple Teachers. AAMAS (2023)
Jizhou Wu, Tianpei Yang, Xiaotian Hao, Jianye Hao, Yan Zheng, Weixun Wang, Matthew E. Taylor
PORTAL: Automatic Curricula Generation for Multiagent Reinforcement Learning. AAMAS (2023)
Manan Tomar, Riashat Islam, Matthew E. Taylor, Sergey Levine, Philip Bachman
Ignorance is Bliss: Robust Control via Information Gating. NeurIPS (2023)
Sriram Ganapathi Subramanian, Matthew E. Taylor, Kate Larson, Mark Crowley
Multi-Agent Advisor Q-Learning. J. Artif. Intell. Res. 74 (2022)
Heng You, Tianpei Yang, Yan Zheng, Jianye Hao, Matthew E. Taylor
Cross-domain adaptive transfer reinforcement learning based on state-action correspondence. UAI (2022)
Taher Jafferjee, Juliusz Krysztof Ziomek, Tianpei Yang, Zipeng Dai, Jianhong Wang, Matthew E. Taylor, Kun Shao, Jun Wang, David Mguni
Semi-Centralised Multi-Agent Reinforcement Learning with Policy-Embedded Training. CoRR (2022)
Sriram Ganapathi Subramanian, Matthew E. Taylor, Mark Crowley, Pascal Poupart
Decentralized Mean Field Games. AAAI (2022)
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
NeurIPS 2022 Competition: Driving SMARTS. CoRR (2022)
Paniz Behboudian, Yash Satsangi, Matthew E. Taylor, Anna Harutyunyan, Michael Bowling
Policy invariant explicit shaping: an efficient alternative to reward shaping. Neural Comput. Appl. 34 (3) (2022)
Yunshu Du, Garrett Warnell, Assefaw H. Gebremedhin, Peter Stone, Matthew E. Taylor
Lucid dreaming for experience replay: refreshing past states with the current policy. Neural Comput. Appl. 34 (3) (2022)
Alex Lewandowski, Calarina Muslimani, Matthew E. Taylor, Jun Luo, Dale Schuurmans
Reinforcement Teaching. CoRR (2022)
Tianyu Zhang, Aakash Krishna G. S, Mohammad Afshari, Petr Musílek, Matthew E. Taylor, Omid Ardakanian
Diversity for transfer in learning-based control of buildings. e-Energy (2022)
Hager Radi, Josiah P. Hanna, Peter Stone, Matthew E. Taylor
Safe Evaluation For Offline Learning: Are We Ready To Deploy? CoRR (2022)
Wenhan Huang, Kai Li, Kun Shao, Tianze Zhou, Matthew E. Taylor, Jun Luo, Dongge Wang, Hangyu Mao, Jianye Hao, Jun Wang, Xiaotie Deng
Multiagent Q-learning with Sub-Team Coordination. NeurIPS (2022)
Michael Guevarra, Srijita Das, Christabel Wayllace, Carrie Demmans Epp, Matthew E. Taylor, Alan Tay
Augmenting Flight Training with AI to Efficiently Train Pilots. CoRR (2022)
Pengyi Li, Hongyao Tang, Tianpei Yang, Xiaotian Hao, Tong Sang, Yan Zheng, Jianye Hao, Matthew E. Taylor, Wenyuan Tao, Zhen Wang
PMIC: Improving Multi-Agent Reinforcement Learning with Progressive Mutual Information Collaboration. ICML (2022)
Pengyi Li, Hongyao Tang, Tianpei Yang, Xiaotian Hao, Tong Sang, Yan Zheng, Jianye Hao, Matthew E. Taylor, Zhen Wang
PMIC: Improving Multi-Agent Reinforcement Learning with Progressive Mutual Information Collaboration. CoRR (2022)
Sahir, Ercüment Ilhan, Srijita Das, Matthew E. Taylor
Methodical Advice Collection and Reuse in Deep Reinforcement Learning. CoRR (2022)
Manan Tomar, Amy Zhang, Roberto Calandra, Matthew E. Taylor, Joelle Pineau
Model-Invariant State Abstractions for Model-Based Reinforcement Learning. CoRR (2021)
Volodymyr Tkachuk, Sriram Ganapathi Subramanian, Matthew E. Taylor
The Effect of Q-function Reuse on the Total Regret of Tabular, Model-Free, Reinforcement Learning. CoRR (2021)
Sai Krishna Gottipati, Yashaswi Pathak, Boris Sattarov, Sahir, Rohan Nuttall, Mohammad Amini, Matthew E. Taylor, Sarath Chandar
Towered Actor Critic For Handling Multiple Action Types In Reinforcement Learning For Drug Discovery. AAAI (2021)
Nikunj Gupta, G. Srinivasaraghavan, Swarup Kumar Mohalik, Matthew E. Taylor
HAMMER: Multi-Level Coordination of Reinforcement Learning Agents via Learned Messaging. CoRR (2021)
Yaodong Yang, Jun Luo, Ying Wen, Oliver Slumbers, Daniel Graves, Haitham Bou-Ammar, Jun Wang, Matthew E. Taylor
Diverse Auto-Curriculum is Critical for Successful Real-World Multiagent Learning Systems. CoRR (2021)
Matthew E. Taylor
Reinforcement Learning for Electronic Design Automation: Successes and Opportunities. ISPD (2021)
Sriram Ganapathi Subramanian, Matthew E. Taylor, Kate Larson, Mark Crowley
Multi-Agent Advisor Q-Learning. CoRR (2021)
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
Driving SMARTS Competition at NeurIPS 2022: Insights and Outcome. NeurIPS (Competition and Demos) (2021)
Sriram Ganapathi Subramanian, Matthew E. Taylor, Mark Crowley, Pascal Poupart
Partially Observable Mean Field Reinforcement Learning. AAMAS (2021)
Yaodong Yang, Jun Luo, Ying Wen, Oliver Slumbers, Daniel Graves, Haitham Bou-Ammar, Jun Wang, Matthew E. Taylor
Diverse Auto-Curriculum is Critical for Successful Real-World Multiagent Learning Systems. AAMAS (2021)
Brittany Davis Pierson, Justine Ventura, Matthew E. Taylor
The Atari Data Scraper. CoRR (2021)
Sriram Ganapathi Subramanian, Matthew E. Taylor, Mark Crowley, Pascal Poupart
Decentralized Mean Field Games. CoRR (2021)
Matthew E. Taylor, Nicholas Nissen, Yuan Wang, Neda Navidi
Improving Reinforcement Learning with Human Assistance: An Argument for Human Subject Studies with HIPPO Gym. CoRR (2021)
Manan Tomar, Utkarsh A. Mishra, Amy Zhang, Matthew E. Taylor
Learning Representations for Pixel-based Control: What Matters and Why? CoRR (2021)
Adam Bignold, Francisco Cruz, Matthew E. Taylor, Tim Brys, Richard Dazeley, Peter Vamplew, Cameron Foale
A Conceptual Framework for Externally-influenced Agents: An Assisted Reinforcement Learning Review. CoRR (2020)
Sriram Ganapathi Subramanian, Pascal Poupart, Matthew E. Taylor, Nidhi Hegde
Multi Type Mean Field Reinforcement Learning. AAMAS (2020)
Pablo Hernandez-Leal, Bilal Kartal, Matthew E. Taylor
A Very Condensed Survey and Critique of Multiagent Deep Reinforcement Learning. AAMAS (2020)
Yang Hu, Diane J. Cook, Matthew E. Taylor
Study of Effectiveness of Prior Knowledge for Smart Home Kit Installation. Sensors 20 (21) (2020)
Yunshu Du, Garrett Warnell, Assefaw Hadish Gebremedhin, Peter Stone, Matthew E. Taylor
Lucid Dreaming for Experience Replay: Refreshing Past States with the Current Policy. CoRR (2020)
Felipe Leno da Silva, Pablo Hernandez-Leal, Bilal Kartal, Matthew E. Taylor
Uncertainty-Aware Action Advising for Deep Reinforcement Learning Agents. AAAI (2020)
Sanmit Narvekar, Bei Peng, Matteo Leonetti, Jivko Sinapov, Matthew E. Taylor, Peter Stone
Curriculum Learning for Reinforcement Learning Domains: A Framework and Survey. CoRR (2020)
Paniz Behboudian, Yash Satsangi, Matthew E. Taylor, Anna Harutyunyan, Michael Bowling
Useful Policy Invariant Shaping from Arbitrary Advice. CoRR (2020)
Sriram Ganapathi Subramanian, Pascal Poupart, Matthew E. Taylor, Nidhi Hegde
Multi Type Mean Field Reinforcement Learning. CoRR (2020)
Sriram Ganapathi Subramanian, Matthew E. Taylor, Mark Crowley, Pascal Poupart
Partially Observable Mean Field Reinforcement Learning. CoRR (2020)
Behzad Ghazanfari, Fatemeh Afghah, Matthew E. Taylor
Sequential Association Rule Mining for Autonomously Extracting Hierarchical Task Structures in Reinforcement Learning. IEEE Access 8 (2020)
Sanmit Narvekar, Bei Peng, Matteo Leonetti, Jivko Sinapov, Matthew E. Taylor, Peter Stone
Curriculum Learning for Reinforcement Learning Domains: A Framework and Survey. J. Mach. Learn. Res. 21 (2020)
Felipe Leno da Silva, Pablo Hernandez-Leal, Bilal Kartal, Matthew E. Taylor
Providing Uncertainty-Based Advice for Deep Reinforcement Learning Agents (Student Abstract). AAAI (2020)
Craig Sherstan, Bilal Kartal, Pablo Hernandez-Leal, Matthew E. Taylor
Work in Progress: Temporally Extended Auxiliary Tasks. CoRR (2020)
Sai Krishna Gottipati, Yashaswi Pathak, Rohan Nuttall, Sahir, Raviteja Chunduru, Ahmed Touati, Sriram Ganapathi Subramanian, Matthew E. Taylor, Sarath Chandar
Maximum Reward Formulation In Reinforcement Learning. CoRR (2020)
Yang Hu, Rachel Min Wong, Olusola O. Adesope, Matthew E. Taylor
Effects of a computer-based learning environment that teaches older adults how to install a smart home system. Comput. Educ. 149 (2020)
Chao Gao, Pablo Hernandez-Leal, Bilal Kartal, Matthew E. Taylor
Skynet: A Top Deep RL Agent in the Inaugural Pommerman Team Competition. CoRR (2019)
Gabriel Victor de la Cruz, Yunshu Du, Matthew E. Taylor
Jointly Pre-training with Supervised, Autoencoder, and Value Losses for Deep Reinforcement Learning. CoRR (2019)
Bilal Kartal, Pablo Hernandez-Leal, Matthew E. Taylor
Terminal Prediction as an Auxiliary Task for Deep Reinforcement Learning. CoRR (2019)
Chao Gao, Bilal Kartal, Pablo Hernandez-Leal, Matthew E. Taylor
On Hard Exploration for Reinforcement Learning: A Case Study in Pommerman. AIIDE (2019)
Pablo Hernandez-Leal, Bilal Kartal, Matthew E. Taylor
Agent Modeling as Auxiliary Task for Deep Reinforcement Learning. AIIDE (2019)
Gabriel Victor de la Cruz, Yunshu Du, Matthew E. Taylor
Pre-training with non-expert human demonstration for deep reinforcement learning. Knowl. Eng. Rev. 34 (2019)
Robert T. Loftin, Bei Peng, Matthew E. Taylor, Michael L. Littman, David L. Roberts
Interactive Learning of Environment Dynamics for Sequential Tasks. CoRR (2019)

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Publication Activity (10 Years)

Top Topics

Top Venues

Matthew E. Taylor