Matthew E. Taylor

According to our database, Matthew E. Taylor authored at least 201 papers between 2005 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2024

Comparing explanations in RL.

Brittany Davis Pierson, Dustin Arendt, John Miller, Matthew E. Taylor

Neural Comput. Appl., January, 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

J. Artif. Intell. Res., 2024

Monitored Markov Decision Processes.

Simone Parisi, Montaser Mohammedalamen, Alireza Kazemipour, Matthew E. Taylor, Michael Bowling

CoRR, 2024

GLIDE-RL: Grounded Language Instruction through DEmonstration in RL.

Chaitanya Kharyal, Sai Krishna Gottipati, Tanmay Kumar Sinha, Srijita Das, Matthew E. Taylor

CoRR, 2024

LaFFi: Leveraging Hybrid Natural Language Feedback for Fine-tuning Language Models.

Qianxi Li, Yingyue Cao, Jikun Kang, Tianpei Yang, Xi Chen, Jun Jin, Matthew E. Taylor

CoRR, 2024

A Transfer Approach Using Graph Neural Networks in Deep Reinforcement Learning.

Tianpei Yang, Heng You, Jianye Hao, Yan Zheng, Matthew E. Taylor

Proceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence, 2024

PORTAL: Automatic Curricula Generation for Multiagent Reinforcement Learning.

Jizhou Wu, Jianye Hao, Tianpei Yang, Xiaotian Hao, Yan Zheng, Weixun Wang, Matthew E. Taylor

Proceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence, 2024

2023

Improving reinforcement learning with human assistance: an argument for human subject studies with HIPPO Gym.

Matthew E. Taylor, Nicholas Nissen, Yuan Wang, Neda Navidi

Neural Comput. Appl., November, 2023

ASN: action semantics network for multiagent reinforcement learning.

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

Auton. Agents Multi Agent Syst., October, 2023

Learning Representations for Pixel-based Control: What Matters and Why?

Manan Tomar, Utkarsh A. Mishra, Amy Zhang, Matthew E. Taylor

Trans. Mach. Learn. Res., 2023

A conceptual framework for externally-influenced agents: an assisted reinforcement learning review.

Adam Bignold, Francisco Cruz, Matthew E. Taylor, Tim Brys, Richard Dazeley, Peter Vamplew, Cameron Foale

J. Ambient Intell. Humaniz. Comput., 2023

MaDi: Learning to Mask Distractions for Generalization in Visual Deep Reinforcement Learning.

Bram Grooten, Tristan Tomilin, Gautham Vasan, Matthew E. Taylor, A. Rupam Mahmood, Meng Fang, Mykola Pechenizkiy, Decebal Constantin Mocanu

CoRR, 2023

Human-AI Collaboration in Real-World Complex Environment with Reinforcement Learning.

Md Saiful Islam, Srijita Das, Sai Krishna Gottipati, William Duguay, Clodéric Mars, Jalal Arabneydi, Antoine Fagette, Matthew Guzdial, Matthew E. Taylor

CoRR, 2023

Curriculum Learning for Cooperation in Multi-Agent Reinforcement Learning.

Rupali Bhati, Sai Krishna Gottipati, Clodéric Mars, Matthew E. Taylor

CoRR, 2023

Human-Machine Teaming for UAVs: An Experimentation Platform.

Laila El Moujtahid, Sai Krishna Gottipati, Clodéric Mars, Matthew E. Taylor

CoRR, 2023

A Call to Arms: AI Should be Critical for Social Media Analysis of Conflict Zones.

Afia Abedin, Abdul Bais, Cody Buntain, Laura Courchesne, Brian McQuinn, Matthew E. Taylor, Muhib Ullah

CoRR, 2023

Ignorance is Bliss: Robust Control via Information Gating.

Manan Tomar, Riashat Islam, Matthew E. Taylor, Sergey Levine, Philip Bachman

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Multi-Agent Advisor Q-Learning (Extended Abstract).

Sriram Ganapathi Subramanian, Matthew E. Taylor, Kate Larson, Mark Crowley

Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence, 2023

Can You Improve My Code? Optimizing Programs with Local Search.

Fatemeh Abdollahi, Saqib Ameen, Matthew E. Taylor, Levi H. S. Lelis

Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence, 2023

Reinforcement Learning Requires Human-in-the-Loop Framing and Approaches.

Matthew E. Taylor

Proceedings of the HHAI 2023: Augmenting Human Intellect, 2023

RL-Ripper: : A Framework for Global Routing Using Reinforcement Learning and Smart Net Ripping Techniques.

Upma Gandhi, Erfan Aghaeekiasaraee, Ismail S. K. Bustany, Payam Mousavi, Matthew E. Taylor, Laleh Behjat

Proceedings of the Great Lakes Symposium on VLSI 2023, 2023

Innovating AI Leadership Education.

Xiaoxue Du, Sharifa Alghowinem, Matthew E. Taylor, Kate Darling, Cynthia Breazeal

Proceedings of the IEEE Frontiers in Education Conference, 2023

Two-Level Actor-Critic Using Multiple Teachers.

Su Zhang, Srijita Das, Sriram Ganapathi Subramanian, Matthew E. Taylor

Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems, 2023

Learning from Multiple Independent Advisors in Multi-agent Reinforcement Learning.

Sriram Ganapathi Subramanian, Matthew E. Taylor, Kate Larson, Mark Crowley

Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems, 2023

Do As You Teach: A Multi-Teacher Approach to Self-Play in Deep Reinforcement Learning.

Chaitanya Kharyal, Tanmay Kumar Sinha, Sai Krishna Gottipati, Fatemeh Abdollahi, Srijita Das, Matthew E. Taylor

Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems, 2023

Automatic Noise Filtering with Dynamic Sparse Training in Deep Reinforcement Learning.

Bram Grooten, Ghada Sokar, Shibhansh Dohare, Elena Mocanu, Matthew E. Taylor, Mykola Pechenizkiy, Decebal Constantin Mocanu

Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems, 2023

Hiking up that HILL with Cogment-Verse: Train & Operate Multi-agent Systems Learning from Humans.

Sai Krishna Gottipati, Luong-Ha Nguyen, Clodéric Mars, Matthew E. Taylor

Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems, 2023

Multi-Robot Warehouse Optimization: Leveraging Machine Learning for Improved Performance.

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

Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems, 2023

C²Tutor: Helping People Learn to Avoid Present Bias During Decision Making.

Calarina Muslimani, Saba Gul, Matthew E. Taylor, Carrie Demmans Epp, Christabel Wayllace

Proceedings of the Artificial Intelligence in Education - 24th International Conference, 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

Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence, 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

Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence, 2023

Augmenting Flight Training with AI to Efficiently Train Pilots.

Michael Guevarra, Srijita Das, Christabel Wayllace, Carrie Demmans Epp, Matthew E. Taylor, Alan Tay

Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence, 2023

2022

Lucid dreaming for experience replay: refreshing past states with the current policy.

Yunshu Du, Garrett Warnell, Assefaw H. Gebremedhin, Peter Stone, Matthew E. Taylor

Neural Comput. Appl., 2022

Policy invariant explicit shaping: an efficient alternative to reward shaping.

Paniz Behboudian, Yash Satsangi, Matthew E. Taylor, Anna Harutyunyan, Michael Bowling

Neural Comput. Appl., 2022

Multi-Agent Advisor Q-Learning.

Sriram Ganapathi Subramanian, Matthew E. Taylor, Kate Larson, Mark Crowley

J. Artif. Intell. Res., 2022

Safe Evaluation For Offline Learning: Are We Ready To Deploy?

Hager Radi, Josiah P. Hanna, Peter Stone, Matthew E. Taylor

CoRR, 2022

NeurIPS 2022 Competition: Driving SMARTS.

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

CoRR, 2022

Semi-Centralised Multi-Agent Reinforcement Learning with Policy-Embedded Training.

Taher Jafferjee, Juliusz Krysztof Ziomek, Tianpei Yang, Zipeng Dai, Jianhong Wang, Matthew E. Taylor, Kun Shao, Jun Wang, David Mguni

CoRR, 2022

Reinforcement Teaching.

Alex Lewandowski, Calarina Muslimani, Matthew E. Taylor, Jun Luo, Dale Schuurmans

CoRR, 2022

Methodical Advice Collection and Reuse in Deep Reinforcement Learning.

Sahir, Ercüment Ilhan, Srijita Das, Matthew E. Taylor

CoRR, 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, Zhen Wang

CoRR, 2022

Cross-domain adaptive transfer reinforcement learning based on state-action correspondence.

Heng You, Tianpei Yang, Yan Zheng, Jianye Hao, Matthew E. Taylor

Proceedings of the Uncertainty in Artificial Intelligence, 2022

Multiagent Q-learning with Sub-Team Coordination.

Wenhan Huang, Kai Li, Kun Shao, Tianze Zhou, Matthew E. Taylor, Jun Luo, Dongge Wang, Hangyu Mao, Jianye Hao, Jun Wang, Xiaotie Deng

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 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

Proceedings of the International Conference on Machine Learning, 2022

Diversity for transfer in learning-based control of buildings.

Tianyu Zhang, Aakash Krishna G. S, Mohammad Afshari, Petr Musílek, Matthew E. Taylor, Omid Ardakanian

Proceedings of the e-Energy '22: The Thirteenth ACM International Conference on Future Energy Systems, Virtual Event, 28 June 2022, 2022

Decentralized Mean Field Games.

Sriram Ganapathi Subramanian, Matthew E. Taylor, Mark Crowley, Pascal Poupart

Proceedings of the Thirty-Sixth AAAI Conference on Artificial Intelligence, 2022

2021

The Atari Data Scraper.

Brittany Davis Pierson, Justine Ventura, Matthew E. Taylor

CoRR, 2021

The Effect of Q-function Reuse on the Total Regret of Tabular, Model-Free, Reinforcement Learning.

Volodymyr Tkachuk, Sriram Ganapathi Subramanian, Matthew E. Taylor

CoRR, 2021

Model-Invariant State Abstractions for Model-Based Reinforcement Learning.

Manan Tomar, Amy Zhang, Roberto Calandra, Matthew E. Taylor, Joelle Pineau

CoRR, 2021

HAMMER: Multi-Level Coordination of Reinforcement Learning Agents via Learned Messaging.

Nikunj Gupta, G. Srinivasaraghavan, Swarup Kumar Mohalik, Matthew E. Taylor

CoRR, 2021

Driving SMARTS Competition at NeurIPS 2022: Insights and Outcome.

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

Proceedings of the NeurIPS 2022 Competition Track, 2021

Reinforcement Learning for Electronic Design Automation: Successes and Opportunities.

Matthew E. Taylor

Proceedings of the ISPD '21: International Symposium on Physical Design, 2021

Diverse Auto-Curriculum is Critical for Successful Real-World Multiagent Learning Systems.

Yaodong Yang, Jun Luo, Ying Wen, Oliver Slumbers, Daniel Graves, Haitham Bou-Ammar, Jun Wang, Matthew E. Taylor

Proceedings of the AAMAS '21: 20th International Conference on Autonomous Agents and Multiagent Systems, 2021

Partially Observable Mean Field Reinforcement Learning.

Sriram Ganapathi Subramanian, Matthew E. Taylor, Mark Crowley, Pascal Poupart

Proceedings of the AAMAS '21: 20th International Conference on Autonomous Agents and Multiagent Systems, 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

Proceedings of the Thirty-Fifth AAAI Conference on Artificial Intelligence, 2021

2020

Study of Effectiveness of Prior Knowledge for Smart Home Kit Installation.

Yang Hu, Diane J. Cook, Matthew E. Taylor

Sensors, 2020

Curriculum Learning for Reinforcement Learning Domains: A Framework and Survey.

Sanmit Narvekar, Bei Peng, Matteo Leonetti, Jivko Sinapov, Matthew E. Taylor, Peter Stone

J. Mach. Learn. Res., 2020

Useful Policy Invariant Shaping from Arbitrary Advice.

Paniz Behboudian, Yash Satsangi, Matthew E. Taylor, Anna Harutyunyan, Michael Bowling

CoRR, 2020

Maximum Reward Formulation In Reinforcement Learning.

Sai Krishna Gottipati, Yashaswi Pathak, Rohan Nuttall, Sahir, Raviteja Chunduru, Ahmed Touati, Sriram Ganapathi Subramanian, Matthew E. Taylor, Sarath Chandar

CoRR, 2020

Work in Progress: Temporally Extended Auxiliary Tasks.

Craig Sherstan, Bilal Kartal, Pablo Hernandez-Leal, Matthew E. Taylor

CoRR, 2020

Effects of a computer-based learning environment that teaches older adults how to install a smart home system.

Yang Hu, Rachel Min Wong, Olusola O. Adesope, Matthew E. Taylor

Comput. Educ., 2020

Sequential Association Rule Mining for Autonomously Extracting Hierarchical Task Structures in Reinforcement Learning.

Behzad Ghazanfari, Fatemeh Afghah, Matthew E. Taylor

IEEE Access, 2020

Multi Type Mean Field Reinforcement Learning.

Sriram Ganapathi Subramanian, Pascal Poupart, Matthew E. Taylor, Nidhi Hegde

Proceedings of the 19th International Conference on Autonomous Agents and Multiagent Systems, 2020

A Very Condensed Survey and Critique of Multiagent Deep Reinforcement Learning.

Pablo Hernandez-Leal, Bilal Kartal, Matthew E. Taylor

Proceedings of the 19th International Conference on Autonomous Agents and Multiagent Systems, 2020

Uncertainty-Aware Action Advising for Deep Reinforcement Learning Agents.

Felipe Leno da Silva, Pablo Hernandez-Leal, Bilal Kartal, Matthew E. Taylor

Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence, 2020

Providing Uncertainty-Based Advice for Deep Reinforcement Learning Agents (Student Abstract).

Felipe Leno da Silva, Pablo Hernandez-Leal, Bilal Kartal, Matthew E. Taylor

Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence, 2020

2019

Analysis of University Fitness Center Data Uncovers Interesting Patterns, Enables Prediction.

Yunshu Du, Assefaw H. Gebremedhin, Matthew E. Taylor

IEEE Trans. Knowl. Data Eng., 2019

Learning to Teach Reinforcement Learning Agents.

Anestis Fachantidis, Matthew E. Taylor, Ioannis P. Vlahavas

Mach. Learn. Knowl. Extr., 2019

Pre-training with non-expert human demonstration for deep reinforcement learning.

Gabriel Victor de la Cruz, Yunshu Du, Matthew E. Taylor

Knowl. Eng. Rev., 2019

Team learning from human demonstration with coordination confidence.

Bikramjit Banerjee, Syamala Vittanala, Matthew Edmund Taylor

Knowl. Eng. Rev., 2019

Interactive Learning of Environment Dynamics for Sequential Tasks.

Robert T. Loftin, Bei Peng, Matthew E. Taylor, Michael L. Littman, David L. Roberts

CoRR, 2019

Skynet: A Top Deep RL Agent in the Inaugural Pommerman Team Competition.

Chao Gao, Pablo Hernandez-Leal, Bilal Kartal, Matthew E. Taylor

CoRR, 2019

Safer Deep RL with Shallow MCTS: A Case Study in Pommerman.

Bilal Kartal, Pablo Hernandez-Leal, Chao Gao, Matthew E. Taylor

CoRR, 2019

Jointly Pre-training with Supervised, Autoencoder, and Value Losses for Deep Reinforcement Learning.

Gabriel Victor de la Cruz, Yunshu Du, Matthew E. Taylor

CoRR, 2019

Robot-enabled support of daily activities in smart home environments.

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

Cogn. Syst. Res., 2019

A survey and critique of multiagent deep reinforcement learning.

Pablo Hernandez-Leal, Bilal Kartal, Matthew E. Taylor

Auton. Agents Multi Agent Syst., 2019

Towers of Saliency: A Reinforcement Learning Visualization Using Immersive Environments.

Nathan Douglas, Dianna Yim, Bilal Kartal, Pablo Hernandez-Leal, Frank Maurer, Matthew E. Taylor

Proceedings of the 2019 ACM International Conference on Interactive Surfaces and Spaces, 2019

Metatrace Actor-Critic: Online Step-Size Tuning by Meta-gradient Descent for Reinforcement Learning Control.

Kenny Young, Baoxiang Wang, Matthew E. Taylor

Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, 2019

Interactive Reinforcement Learning with Dynamic Reuse of Prior Knowledge from Human and Agent Demonstrations.

Zhaodong Wang, Matthew E. Taylor

Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, 2019

Achieving cooperation through deep multiagent reinforcement learning in sequential prisoner's dilemmas.

Weixun Wang, Jianye Hao, Yixi Wang, Matthew E. Taylor

Proceedings of the First International Conference on Distributed Artificial Intelligence, 2019

Action Guidance with MCTS for Deep Reinforcement Learning.

Bilal Kartal, Pablo Hernandez-Leal, Matthew E. Taylor

Proceedings of the Fifteenth AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment, 2019

Terminal Prediction as an Auxiliary Task for Deep Reinforcement Learning.

Bilal Kartal, Pablo Hernandez-Leal, Matthew E. Taylor

Proceedings of the Fifteenth AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment, 2019

Agent Modeling as Auxiliary Task for Deep Reinforcement Learning.

Pablo Hernandez-Leal, Bilal Kartal, Matthew E. Taylor

Proceedings of the Fifteenth AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment, 2019

On Hard Exploration for Reinforcement Learning: A Case Study in Pommerman.

Chao Gao, Bilal Kartal, Pablo Hernandez-Leal, Matthew E. Taylor

Proceedings of the Fifteenth AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment, 2019

2018

Curriculum Design for Machine Learners in Sequential Decision Tasks.

Bei Peng, James MacGlashan, Robert Tyler Loftin, Michael L. Littman, David L. Roberts, Matthew E. Taylor

IEEE Trans. Emerg. Top. Comput. Intell., 2018

Leveraging human knowledge in tabular reinforcement learning: a study of human subjects.

Ariel Rosenfeld, Moshe Cohen, Matthew E. Taylor, Sarit Kraus

Knowl. Eng. Rev., 2018

Using Monte Carlo Tree Search as a Demonstrator within Asynchronous Deep RL.

Bilal Kartal, Pablo Hernandez-Leal, Matthew E. Taylor

CoRR, 2018

Autonomous Extraction of a Hierarchical Structure of Tasks in Reinforcement Learning, A Sequential Associate Rule Mining Approach.

Behzad Ghazanfari, Fatemeh Afghah, Matthew E. Taylor

CoRR, 2018

Is multiagent deep reinforcement learning the answer or the question? A brief survey.

Pablo Hernandez-Leal, Bilal Kartal, Matthew E. Taylor

CoRR, 2018

Metatrace: Online Step-size Tuning by Meta-gradient Descent for Reinforcement Learning Control.

Kenny Young, Baoxiang Wang, Matthew E. Taylor

CoRR, 2018

Interactive Reinforcement Learning with Dynamic Reuse of Prior Knowledge from Human/Agent's Demonstration.

Zhaodong Wang, Matthew E. Taylor

CoRR, 2018

Towards Cooperation in Sequential Prisoner's Dilemmas: a Deep Multiagent Reinforcement Learning Approach.

Weixun Wang, Jianye Hao, Yixi Wang, Matthew E. Taylor

CoRR, 2018

Improving Reinforcement Learning with Human Input.

Matthew E. Taylor

Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence, 2018

Autonomously Reusing Knowledge in Multiagent Reinforcement Learning.

Felipe Leno da Silva, Matthew E. Taylor, Anna Helena Reali Costa

Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence, 2018

2017

Bin-Dog: A Robotic Platform for Bin Management in Orchards.

Yunxiang Ye, Zhaodong Wang, Dylan Jones, Long He, Matthew E. Taylor, Geoffrey A. Hollinger, Qin Zhang

Robotics, 2017

Scalable lifelong reinforcement learning.

Yusen Zhan, Haitham Bou-Ammar, Matthew E. Taylor

Pattern Recognit., 2017

Nonconvex Policy Search Using Variational Inequalities.

Yusen Zhan, Haitham Bou-Ammar, Matthew E. Taylor

Neural Comput., 2017

Multi-objectivization and ensembles of shapings in reinforcement learning.

Tim Brys, Anna Harutyunyan, Peter Vrancx, Ann Nowé, Matthew E. Taylor

Neurocomputing, 2017

Autonomous Extracting a Hierarchical Structure of Tasks in Reinforcement Learning and Multi-task Reinforcement Learning.

Behzad Ghazanfari, Matthew E. Taylor

CoRR, 2017

Pre-training Neural Networks with Human Demonstrations for Deep Reinforcement Learning.

Gabriel Victor de la Cruz, Yunshu Du, Matthew E. Taylor

CoRR, 2017

Interactive Learning from Policy-Dependent Human Feedback.

James MacGlashan, Mark K. Ho, Robert Tyler Loftin, Bei Peng, David L. Roberts, Matthew E. Taylor, Michael L. Littman

CoRR, 2017

An exploration strategy for non-stationary opponents.

Pablo Hernandez-Leal, Yusen Zhan, Matthew E. Taylor, Luis Enrique Sucar, Enrique Munoz de Cote

Auton. Agents Multi Agent Syst., 2017

Efficiently detecting switches against non-stationary opponents.

Pablo Hernandez-Leal, Yusen Zhan, Matthew E. Taylor, Luis Enrique Sucar, Enrique Munoz de Cote

Auton. Agents Multi Agent Syst., 2017

Improving Reinforcement Learning with Confidence-Based Demonstrations.

Zhaodong Wang, Matthew E. Taylor

Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence, 2017

Leveraging Human Knowledge in Tabular Reinforcement Learning: A Study of Human Subjects.

Ariel Rosenfeld, Matthew E. Taylor, Sarit Kraus

Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence, 2017

Interactive Learning from Policy-Dependent Human Feedback.

James MacGlashan, Mark K. Ho, Robert Tyler Loftin, Bei Peng, Guan Wang, David L. Roberts, Matthew E. Taylor, Michael L. Littman

Proceedings of the 34th International Conference on Machine Learning, 2017

Speeding up Tabular Reinforcement Learning Using State-Action Similarities.

Ariel Rosenfeld, Matthew E. Taylor, Sarit Kraus

Proceedings of the 16th Conference on Autonomous Agents and MultiAgent Systems, 2017

An Exploration Strategy Facing Non-Stationary Agents.

Pablo Hernandez-Leal, Yusen Zhan, Matthew E. Taylor, Luis Enrique Sucar, Enrique Munoz de Cote

Proceedings of the 16th Conference on Autonomous Agents and MultiAgent Systems, 2017

Detecting Switches Against Non-Stationary Opponents.

Pablo Hernandez-Leal, Yusen Zhan, Matthew E. Taylor, Luis Enrique Sucar, Enrique Munoz de Cote

Proceedings of the 16th Conference on Autonomous Agents and MultiAgent Systems, 2017

Solar Decathlon Competition: Towards a Solar-Powered Smart Home.

Amanda Leah Zulas, Kaitlyn I. Franz, Darrin Griechen, Matthew E. Taylor

Proceedings of the Workshops of the The Thirty-First AAAI Conference on Artificial Intelligence, 2017

AI Projects for Computer Science Capstone Classes (Extended Abstract).

Matthew E. Taylor, Sakire Arslan Ay

Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence, 2017

Scalable Multitask Policy Gradient Reinforcement Learning.

Salam El Bsat, Haitham Bou-Ammar, Matthew E. Taylor

Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence, 2017

2016

Learning behaviors via human-delivered discrete feedback: modeling implicit feedback strategies to speed up learning.

Robert T. Loftin, Bei Peng, James MacGlashan, Michael L. Littman, Matthew E. Taylor, Jeff Huang, David L. Roberts

Auton. Agents Multi Agent Syst., 2016

Lifelong learning for disturbance rejection on mobile robots.

David Isele, José-Marcio Luna, Eric Eaton, Gabriel Victor de la Cruz, James Irwin, Brandon Kallaher, Matthew E. Taylor

Proceedings of the 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems, 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

Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence, 2016

Learning from Demonstration for Shaping through Inverse Reinforcement Learning.

Halit Bener Suay, Tim Brys, Matthew E. Taylor, Sonia Chernova

Proceedings of the 2016 International Conference on Autonomous Agents & Multiagent Systems, 2016

A Need for Speed: Adapting Agent Action Speed to Improve Task Learning from Non-Expert Humans.

Bei Peng, James MacGlashan, Robert Tyler Loftin, Michael L. Littman, David L. Roberts, Matthew E. Taylor

Proceedings of the 2016 International Conference on Autonomous Agents & Multiagent Systems, 2016

A Bayesian Approach for Learning and Tracking Switching, Non-Stationary Opponents: (Extended Abstract).

Pablo Hernandez-Leal, Benjamin Rosman, Matthew E. Taylor, Luis Enrique Sucar, Enrique Munoz de Cote

Proceedings of the 2016 International Conference on Autonomous Agents & Multiagent Systems, 2016

Effective Transfer via Demonstrations in Reinforcement Learning: A Preliminary Study.

Zhaodong Wang, Matthew E. Taylor

Proceedings of the 2016 AAAI Spring Symposia, 2016

Towards Behavior-Aware Model Learning from Human-Generated Trajectories.

Robert Tyler Loftin, James MacGlashan, Bei Peng, Matthew E. Taylor, Michael L. Littman, David L. Roberts

Proceedings of the 2016 AAAI Fall Symposia, Arlington, Virginia, USA, November 17-19, 2016, 2016

Dimensionality Reduced Reinforcement Learning for Assistive Robots.

William Curran, Tim Brys, David W. Aha, Matthew E. Taylor, William D. Smart

Proceedings of the 2016 AAAI Fall Symposia, Arlington, Virginia, USA, November 17-19, 2016, 2016

Identifying and Tracking Switching, Non-Stationary Opponents: A Bayesian Approach.

Pablo Hernandez-Leal, Matthew E. Taylor, Benjamin Rosman, Luis Enrique Sucar, Enrique Munoz de Cote

Proceedings of the Multiagent Interaction without Prior Coordination, 2016

2015

Using PCA to Efficiently Represent State Spaces.

William Curran, Tim Brys, Matthew E. Taylor, William D. Smart

CoRR, 2015

Transfer learning with probabilistic mapping selection.

Anestis Fachantidis, Ioannis Partalas, Matthew E. Taylor, Ioannis P. Vlahavas

Adapt. Behav., 2015

Towards Integrating Real-Time Crowd Advice with Reinforcement Learning.

Gabriel Victor de la Cruz, Bei Peng, Walter S. Lasecki, Matthew E. Taylor

Proceedings of the 20th International Conference on Intelligent User Interfaces Companion, 2015

Reinforcement Learning from Demonstration through Shaping.

Tim Brys, Anna Harutyunyan, Halit Bener Suay, Sonia Chernova, Matthew E. Taylor, Ann Nowé

Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence, 2015

Bidding in Non-Stationary Energy Markets.

Pablo Hernandez-Leal, Matthew E. Taylor, Enrique Munoz de Cote, Luis Enrique Sucar

Proceedings of the 2015 International Conference on Autonomous Agents and Multiagent Systems, 2015

Policy Transfer using Reward Shaping.

Tim Brys, Anna Harutyunyan, Matthew E. Taylor, Ann Nowé

Proceedings of the 2015 International Conference on Autonomous Agents and Multiagent Systems, 2015

Online Transfer Learning in Reinforcement Learning Domains.

Yusen Zhan, Matthew E. Taylor

Proceedings of the 2015 AAAI Fall Symposia, Arlington, Virginia, USA, November 12-14, 2015, 2015

On the Ability to Provide Demonstrations on a UAS: Observing 90 Untrained Participants Abusing a Flying Robot.

Mitchell Scott, Bei Peng, Madeline Chili, Tanay Nigam, Francis G. Pascual, Cynthia Matuszek, Matthew E. Taylor

Proceedings of the 2015 AAAI Fall Symposia, Arlington, Virginia, USA, November 12-14, 2015, 2015

Generating Real-Time Crowd Advice to Improve Reinforcement Learning Agents.

Gabriel Victor de la Cruz, Bei Peng, Walter Stephen Lasecki, Matthew Edmund Taylor

Proceedings of the Learning for General Competency in Video Games, 2015

Unsupervised Cross-Domain Transfer in Policy Gradient Reinforcement Learning via Manifold Alignment.

Haitham Bou-Ammar, Eric Eaton, Paul Ruvolo, Matthew E. Taylor

Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence, 2015

2014

Reinforcement learning agents providing advice in complex video games.

Matthew E. Taylor, Nicholas Carboni, Anestis Fachantidis, Ioannis P. Vlahavas, Lisa Torrey

Connect. Sci., 2014

Distributed learning and multi-objectivity in traffic light control.

Tim Brys, Tong T. Pham, Matthew E. Taylor

Connect. Sci., 2014

CLEAN Rewards to Improve Coordination by Removing Exploratory Action Noise.

Chris HolmesParker, Matthew E. Taylor, Adrian K. Agogino, Kagan Tumer

Proceedings of the 2014 IEEE/WIC/ACM International Joint Conferences on Web Intelligence (WI) and Intelligent Agent Technologies (IAT), Warsaw, Poland, August 11-14, 2014, 2014

An Autonomous Transfer Learning Algorithm for TD-Learners.

Anestis Fachantidis, Ioannis Partalas, Matthew E. Taylor, Ioannis P. Vlahavas

Proceedings of the Artificial Intelligence: Methods and Applications, 2014

Learning something from nothing: Leveraging implicit human feedback strategies.

Robert Tyler Loftin, Bei Peng, James MacGlashan, Michael L. Littman, Matthew E. Taylor, Jeff Huang, David L. Roberts

Proceedings of the 23rd IEEE International Symposium on Robot and Human Interactive Communication, 2014

Agents Teaching Agents in Reinforcement Learning (Nectar Abstract).

Matthew E. Taylor, Lisa Torrey

Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2014

Multi-objectivization of reinforcement learning problems by reward shaping.

Tim Brys, Anna Harutyunyan, Peter Vrancx, Matthew E. Taylor, Daniel Kudenko, Ann Nowé

Proceedings of the 2014 International Joint Conference on Neural Networks, 2014

Online Multi-Task Learning for Policy Gradient Methods.

Haitham Bou-Ammar, Eric Eaton, Paul Ruvolo, Matthew E. Taylor

Proceedings of the 31th International Conference on Machine Learning, 2014

Using Ensemble Techniques and Multi-Objectivization to Solve Reinforcement Learning Problems.

Tim Brys, Matthew E. Taylor, Ann Nowé

Proceedings of the ECAI 2014 - 21st European Conference on Artificial Intelligence, 18-22 August 2014, Prague, Czech Republic, 2014

CLEANing the reward: counterfactual actions to remove exploratory action noise in multiagent learning (extended abstract).

Chris HolmesParker, Matthew E. Taylor, Adrian K. Agogino, Kagan Tumer

Proceedings of the International conference on Autonomous Agents and Multi-Agent Systems, 2014

Adaptive objective selection for correlated objectives in multi-objective reinforcement learning.

Tim Brys, Kristof Van Moffaert, Ann Nowé, Matthew E. Taylor

Proceedings of the International conference on Autonomous Agents and Multi-Agent Systems, 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

Proceedings of the Twenty-Eighth AAAI Conference on Artificial Intelligence, 2014

Combining Multiple Correlated Reward and Shaping Signals by Measuring Confidence.

Tim Brys, Ann Nowé, Daniel Kudenko, Matthew E. Taylor

Proceedings of the Twenty-Eighth AAAI Conference on Artificial Intelligence, 2014

2013

Mitigating multi-path fading in a mobile mesh network.

Marcos Augusto M. Vieira, Matthew E. Taylor, Prateek Tandon, Manish Jain, Ramesh Govindan, Gaurav S. Sukhatme, Milind Tambe

Ad Hoc Networks, 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

Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2013

Teaching on a budget: agents advising agents in reinforcement learning.

Lisa Torrey, Matthew E. Taylor

Proceedings of the International conference on Autonomous Agents and Multi-Agent Systems, 2013

Autonomous Selection of Inter-Task Mappings in Transfer Learning (extended abstract).

Anestis Fachantidis, Ioannis Partalas, Matthew E. Taylor, Ioannis P. Vlahavas

Proceedings of the Lifelong Machine Learning, 2013

Learning for Mobile-Robot Error Recovery (Extended Abstract).

Ravi Balasubramanian, Matthew E. Taylor

Proceedings of the Designing Intelligent Robots: Reintegrating AI II, 2013

2012

Towards student/teacher learning in sequential decision tasks.

Lisa Torrey, Matthew E. Taylor

Proceedings of the International Conference on Autonomous Agents and Multiagent Systems, 2012

Reinforcement learning transfer via sparse coding.

Haitham Bou-Ammar, Karl Tuyls, Matthew E. Taylor, Kurt Driessens, Gerhard Weiss

Proceedings of the International Conference on Autonomous Agents and Multiagent Systems, 2012

2011

An Introduction to Intertask Transfer for Reinforcement Learning.

Matthew E. Taylor, Peter Stone

AI Mag., 2011

Distributed on-Line Multi-Agent Optimization under Uncertainty: Balancing Exploration and Exploitation.

Matthew E. Taylor, Manish Jain, Prateek Tandon, Makoto Yokoo, Milind Tambe

Adv. Complex Syst., 2011

Editorial: Agents and Multi-Agent Systems.

Frank Schweitzer, Matthew E. Taylor

Adv. Complex Syst., 2011

Towards Addressing Model Uncertainty: Robust Execution-Time Coordination for Teamwork.

Jun-young Kwak, Rong Yang, Zhengyu Yin, Matthew E. Taylor, Milind Tambe

Proceedings of the 2011 IEEE/WIC/ACM International Conference on Intelligent Agent Technology, 2011

Transfer Learning via Multiple Inter-task Mappings.

Anestis Fachantidis, Ioannis Partalas, Matthew E. Taylor, Ioannis P. Vlahavas

Proceedings of the Recent Advances in Reinforcement Learning - 9th European Workshop, 2011

Reinforcement Learning Transfer Using a Sparse Coded Inter-task Mapping.

Haitham Bou-Ammar, Matthew E. Taylor, Karl Tuyls, Gerhard Weiss

Proceedings of the Multi-Agent Systems - 9th European Workshop, 2011

Teaching Reinforcement Learning with Mario: An Argument and Case Study.

Matthew Edmund Taylor

Proceedings of the Second Symposium on Education Advances in Artificial Intelligence, 2011

Model AI Assignments 2011.

Todd W. Neller, Marie desJardins, Tim Oates, Matthew E. Taylor

Proceedings of the Second Symposium on Education Advances in Artificial Intelligence, 2011

ESCAPES: evacuation simulation with children, authorities, parents, emotions, and social comparison.

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

Proceedings of the 10th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2011), 2011

Integrating reinforcement learning with human demonstrations of varying ability.

Matthew E. Taylor, Halit Bener Suay, Sonia Chernova

Proceedings of the 10th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2011), 2011

Metric learning for reinforcement learning agents.

Matthew E. Taylor, Brian Kulis, Fei Sha

Proceedings of the 10th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2011), 2011

Flood Disaster Mitigation: A Real-World Challenge Problem for Multi-agent Unmanned Surface Vehicles.

Paul Scerri, Balajee Kannan, Prasanna Velagapudi, Kate Macarthur, Peter Stone, Matthew E. Taylor, John Dolan, Alessandro Farinelli, Archie C. Chapman, Bernadine Dias, George Kantor

Proceedings of the Advanced Agent Technology, 2011

Teamwork in distributed POMDPs: execution-time coordination under model uncertainty.

Jun-young Kwak, Rong Yang, Zhengyu Yin, Matthew E. Taylor, Milind Tambe

Proceedings of the 10th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2011), 2011

Reinforcement Learning Transfer via Common Subspaces.

Haitham Bou-Ammar, Matthew E. Taylor

Proceedings of the Adaptive and Learning Agents - International Workshop, 2011

Protecting against evaluation overfitting in empirical reinforcement learning.

Shimon Whiteson, Brian Tanner, Matthew E. Taylor, Peter Stone

Proceedings of the 2011 IEEE Symposium on Adaptive Dynamic Programming And Reinforcement Learning, 2011

Using Human Demonstrations to Improve Reinforcement Learning.

Matthew Edmund Taylor, Halit Bener Suay, Sonia Chernova

Proceedings of the Help Me Help You: Bridging the Gaps in Human-Agent Collaboration, 2011

2010

A Framework for Evaluating Deployed Security Systems: Is There a Chink in your ARMOR?

Matthew E. Taylor, Christopher Kiekintveld, Craig Western, Milind Tambe

Informatica (Slovenia), 2010

Critical factors in the empirical performance of temporal difference and evolutionary methods for reinforcement learning.

Shimon Whiteson, Matthew E. Taylor, Peter Stone

Auton. Agents Multi Agent Syst., 2010

When should there be a "Me" in "Team"?: distributed multi-agent optimization under uncertainty.

Matthew E. Taylor, Manish Jain, Yanquin Jin, Makoto Yokoo, Milind Tambe

Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2010), 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

Proceedings of the Twenty-Fourth AAAI Conference on Artificial Intelligence, 2010

Teamwork and Coordination under Model Uncertainty in DEC-POMDPs.

Jun-young Kwak, Rong Yang, Zhengyu Yin, Matthew E. Taylor, Milind Tambe

Proceedings of the Interactive Decision Theory and Game Theory, 2010

2009

Transfer in Reinforcement Learning Domains

Matthew E. Taylor

Studies in Computational Intelligence 216, Springer, ISBN: 978-3-642-01881-7, 2009

Transfer Learning for Reinforcement Learning Domains: A Survey.

Matthew E. Taylor, Peter Stone

J. Mach. Learn. Res., 2009

AAAI 2008 Workshop Reports.

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

AI Mag., 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

Proceedings of the IJCAI 2009, 2009

Abstraction and Generalization in Reinforcement Learning: A Summary and Framework.

Marc J. V. Ponsen, Matthew E. Taylor, Karl Tuyls

Proceedings of the Adaptive and Learning Agents, Second Workshop, 2009

Exploiting Coordination Locales in Distributed POMDPs via Social Model Shaping.

Pradeep Varakantham, Jun-young Kwak, Matthew E. Taylor, Janusz Marecki, Paul Scerri, Milind Tambe

Proceedings of the 19th International Conference on Automated Planning and Scheduling, 2009

Assisting Transfer-Enabled Machine Learning Algorithms: Leveraging Human Knowledge for Curriculum Design.

Matthew E. Taylor

Proceedings of the Agents that Learn from Human Teachers, 2009

2008

Transferring Instances for Model-Based Reinforcement Learning.

Matthew E. Taylor, Nicholas K. Jong, Peter Stone

Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2008

Autonomous transfer for reinforcement learning.

Matthew E. Taylor, Gregory Kuhlmann, Peter Stone

Proceedings of the 7th International Joint Conference on Autonomous Agents and Multiagent Systems (AAMAS 2008), 2008

Transfer Learning and Intelligence: an Argument and Approach.

Matthew E. Taylor, Gregory Kuhlmann, Peter Stone

Proceedings of the Artificial General Intelligence 2008, 2008

Feature selection and policy optimization for distributed instruction placement using reinforcement learning.

Katherine E. Coons, Behnam Robatmili, Matthew E. Taylor, Bertrand A. Maher, Doug Burger, Kathryn S. McKinley

Proceedings of the 17th International Conference on Parallel Architectures and Compilation Techniques, 2008

2007

Transfer Learning via Inter-Task Mappings for Temporal Difference Learning.

Matthew E. Taylor, Peter Stone, Yaxin Liu

J. Mach. Learn. Res., 2007

Empirical Studies in Action Selection with Reinforcement Learning.

Shimon Whiteson, Matthew E. Taylor, Peter Stone

Adapt. Behav., 2007

Cross-domain transfer for reinforcement learning.

Matthew E. Taylor, Peter Stone

Proceedings of the Machine Learning, 2007

Guiding Inference with Policy Search Reinforcement Learning.

Matthew E. Taylor, Cynthia Matuszek, Pace Reagan Smith, Michael Witbrock

Proceedings of the Twentieth International Florida Artificial Intelligence Research Society Conference, 2007

Autonomous Classification of Knowledge into an Ontology.

Matthew E. Taylor, Cynthia Matuszek, Bryan Klimt, Michael Witbrock

Proceedings of the Twentieth International Florida Artificial Intelligence Research Society Conference, 2007

Transfer via inter-task mappings in policy search reinforcement learning.

Matthew E. Taylor, Shimon Whiteson, Peter Stone

Proceedings of the 6th International Joint Conference on Autonomous Agents and Multiagent Systems (AAMAS 2007), 2007

Towards reinforcement learning representation transfer.

Matthew E. Taylor, Peter Stone

Proceedings of the 6th International Joint Conference on Autonomous Agents and Multiagent Systems (AAMAS 2007), 2007

IFSA: incremental feature-set augmentation for reinforcement learning tasks.

Mazda Ahmadi, Matthew E. Taylor, Peter Stone

Proceedings of the 6th International Joint Conference on Autonomous Agents and Multiagent Systems (AAMAS 2007), 2007

Representation Transfer for Reinforcement Learning.

Matthew E. Taylor, Peter Stone

Proceedings of the Computational Approaches to Representation Change during Learning and Development, 2007

Temporal Difference and Policy Search Methods for Reinforcement Learning: An Empirical Comparison.

Matthew E. Taylor, Shimon Whiteson, Peter Stone

Proceedings of the Twenty-Second AAAI Conference on Artificial Intelligence, 2007

Representation Transfer via Elaboration.

Matthew E. Taylor, Peter Stone

Proceedings of the Twenty-Second AAAI Conference on Artificial Intelligence, 2007

Autonomous Inter-Task Transfer in Reinforcement Learning Domains.

Matthew E. Taylor

Proceedings of the Twenty-Second AAAI Conference on Artificial Intelligence, 2007

2006

Comparing evolutionary and temporal difference methods in a reinforcement learning domain.

Matthew E. Taylor, Shimon Whiteson, Peter Stone

Proceedings of the Genetic and Evolutionary Computation Conference, 2006

Inter-Task Action Correlation for Reinforcement Learning Tasks.

Matthew E. Taylor, Peter Stone

Proceedings of the Proceedings, 2006

2005

Keepaway Soccer: From Machine Learning Testbed to Benchmark.

Peter Stone, Gregory Kuhlmann, Matthew E. Taylor, Yaxin Liu

Proceedings of the RoboCup 2005: Robot Soccer World Cup IX, 2005

Behavior transfer for value-function-based reinforcement learning.

Matthew E. Taylor, Peter Stone

Proceedings of the 4th International Joint Conference on Autonomous Agents and Multiagent Systems (AAMAS 2005), 2005

Value Functions for RL-Based Behavior Transfer: A Comparative Study.

Matthew E. Taylor, Peter Stone, Yaxin Liu

Proceedings of the Proceedings, 2005