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2020 – today
- 2023
[j37]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): 3621-3644 (2023)- [j36]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) - [c112]Sriram Ganapathi Subramanian, Matthew E. Taylor, Kate Larson, Mark Crowley:
Learning from Multiple Independent Advisors in Multi-agent Reinforcement Learning. AAMAS 2023: 1144-1153 - [c111]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: 1932-1941 - [c110]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: 2457-2459 - [c109]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: 2460-2462 - [c108]Su Zhang, Srijita Das, Sriram Ganapathi Subramanian, Matthew E. Taylor:
Two-Level Actor-Critic Using Multiple Teachers. AAMAS 2023: 2589-2591 - [c107]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: 3047-3049 - [c106]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: 3065-3067 - [c105]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: 197-201 - [i49]Sriram Ganapathi Subramanian, Matthew E. Taylor, Kate Larson, Mark Crowley:
Learning from Multiple Independent Advisors in Multi-agent Reinforcement Learning. CoRR abs/2301.11153 (2023) - [i48]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 abs/2302.06548 (2023) - 2022
- [j35]Sriram Ganapathi Subramanian, Matthew E. Taylor, Kate Larson, Mark Crowley:
Multi-Agent Advisor Q-Learning. J. Artif. Intell. Res. 74: 1-74 (2022) - [j34]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): 1673-1686 (2022) - [j33]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): 1687-1712 (2022) - [c104]Sriram Ganapathi Subramanian, Matthew E. Taylor, Mark Crowley, Pascal Poupart:
Decentralized Mean Field Games. AAAI 2022: 9439-9447 - [c103]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: 556-564 - [c102]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: 12979-12997 - [c101]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 - [c100]Heng You, Tianpei Yang, Yan Zheng, Jianye Hao, Matthew E. Taylor:
Cross-domain adaptive transfer reinforcement learning based on state-action correspondence. UAI 2022: 2299-2309 - [e4]Piotr Faliszewski, Viviana Mascardi, Catherine Pelachaud, Matthew E. Taylor:
21st International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2022, Auckland, New Zealand, May 9-13, 2022. International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS) 2022, ISBN 978-1-4503-9213-6 [contents] - [i47]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 abs/2203.08553 (2022) - [i46]Sahir, Ercüment Ilhan, Srijita Das, Matthew E. Taylor:
Methodical Advice Collection and Reuse in Deep Reinforcement Learning. CoRR abs/2204.07254 (2022) - [i45]Alex Lewandowski, Calarina Muslimani, Matthew E. Taylor, Jun Luo, Dale Schuurmans:
Reinforcement Teaching. CoRR abs/2204.11897 (2022) - [i44]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 abs/2209.01054 (2022) - [i43]Michael Guevarra, Srijita Das, Christabel Wayllace, Carrie Demmans Epp, Matthew E. Taylor, Alan Tay:
Augmenting Flight Training with AI to Efficiently Train Pilots. CoRR abs/2210.06683 (2022) - [i42]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 abs/2211.07545 (2022) - [i41]Hager Radi, Josiah P. Hanna, Peter Stone, Matthew E. Taylor:
Safe Evaluation For Offline Learning: Are We Ready To Deploy? CoRR abs/2212.08302 (2022) - 2021
- [c99]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: 142-150 - [c98]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: 51-56 - [c97]Sriram Ganapathi Subramanian, Matthew E. Taylor, Mark Crowley, Pascal Poupart:
Partially Observable Mean Field Reinforcement Learning. AAMAS 2021: 537-545 - [c96]Matthew E. Taylor:
Reinforcement Learning for Electronic Design Automation: Successes and Opportunities. ISPD 2021: 3 - [i40]Nikunj Gupta, G. Srinivasaraghavan, Swarup Kumar Mohalik, Matthew E. Taylor:
HAMMER: Multi-Level Coordination of Reinforcement Learning Agents via Learned Messaging. CoRR abs/2102.00824 (2021) - [i39]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 abs/2102.02639 (2021) - [i38]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 abs/2102.07659 (2021) - [i37]Manan Tomar, Amy Zhang, Roberto Calandra, Matthew E. Taylor, Joelle Pineau:
Model-Invariant State Abstractions for Model-Based Reinforcement Learning. CoRR abs/2102.09850 (2021) - [i36]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 abs/2103.04416 (2021) - [i35]Brittany Davis Pierson, Justine Ventura, Matthew E. Taylor:
The Atari Data Scraper. CoRR abs/2104.04893 (2021) - [i34]Sriram Ganapathi Subramanian, Matthew E. Taylor, Kate Larson, Mark Crowley:
Multi-Agent Advisor Q-Learning. CoRR abs/2111.00345 (2021) - [i33]Manan Tomar, Utkarsh A. Mishra, Amy Zhang, Matthew E. Taylor:
Learning Representations for Pixel-based Control: What Matters and Why? CoRR abs/2111.07775 (2021) - [i32]Sriram Ganapathi Subramanian, Matthew E. Taylor, Mark Crowley, Pascal Poupart:
Decentralized Mean Field Games. CoRR abs/2112.09099 (2021) - 2020
- [j32]Behzad Ghazanfari, Fatemeh Afghah, Matthew E. Taylor:
Sequential Association Rule Mining for Autonomously Extracting Hierarchical Task Structures in Reinforcement Learning. IEEE Access 8: 11782-11799 (2020) - [j31]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: 103816 (2020) - [j30]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: 181:1-181:50 (2020) - [j29]Yang Hu, Diane J. Cook, Matthew E. Taylor:
Study of Effectiveness of Prior Knowledge for Smart Home Kit Installation. Sensors 20(21): 6145 (2020) - [c95]Felipe Leno da Silva, Pablo Hernandez-Leal, Bilal Kartal, Matthew E. Taylor:
Uncertainty-Aware Action Advising for Deep Reinforcement Learning Agents. AAAI 2020: 5792-5799 - [c94]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: 13913-13914 - [c93]Sriram Ganapathi Subramanian, Pascal Poupart, Matthew E. Taylor, Nidhi Hegde:
Multi Type Mean Field Reinforcement Learning. AAMAS 2020: 411-419 - [c92]Pablo Hernandez-Leal, Bilal Kartal, Matthew E. Taylor:
A Very Condensed Survey and Critique of Multiagent Deep Reinforcement Learning. AAMAS 2020: 2146-2148 - [e3]Matthew E. Taylor, Yang Yu, Edith Elkind, Yang Gao:
Distributed Artificial Intelligence - Second International Conference, DAI 2020, Nanjing, China, October 24-27, 2020, Proceedings. Lecture Notes in Computer Science 12547, Springer 2020, ISBN 978-3-030-64095-8 [contents] - [i31]Sriram Ganapathi Subramanian, Pascal Poupart, Matthew E. Taylor, Nidhi Hegde:
Multi Type Mean Field Reinforcement Learning. CoRR abs/2002.02513 (2020) - [i30]Sanmit Narvekar, Bei Peng, Matteo Leonetti, Jivko Sinapov, Matthew E. Taylor, Peter Stone:
Curriculum Learning for Reinforcement Learning Domains: A Framework and Survey. CoRR abs/2003.04960 (2020) - [i29]Craig Sherstan, Bilal Kartal, Pablo Hernandez-Leal, Matthew E. Taylor:
Work in Progress: Temporally Extended Auxiliary Tasks. CoRR abs/2004.00600 (2020) - [i28]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 abs/2007.01544 (2020) - [i27]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 abs/2009.13736 (2020) - [i26]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 abs/2010.03744 (2020) - [i25]Paniz Behboudian, Yash Satsangi, Matthew E. Taylor, Anna Harutyunyan, Michael Bowling:
Useful Policy Invariant Shaping from Arbitrary Advice. CoRR abs/2011.01297 (2020) - [i24]Sriram Ganapathi Subramanian, Matthew E. Taylor, Mark Crowley, Pascal Poupart:
Partially Observable Mean Field Reinforcement Learning. CoRR abs/2012.15791 (2020)
2010 – 2019
- 2019
- [j28]Pablo Hernandez-Leal, Bilal Kartal, Matthew E. Taylor:
A survey and critique of multiagent deep reinforcement learning. Auton. Agents Multi Agent Syst. 33(6): 750-797 (2019) - [j27]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:
Robot-enabled support of daily activities in smart home environments. Cogn. Syst. Res. 54: 258-272 (2019) - [j26]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: e10 (2019) - [j25]Bikramjit Banerjee, Syamala Vittanala, Matthew Edmund Taylor:
Team learning from human demonstration with coordination confidence. Knowl. Eng. Rev. 34: e12 (2019) - [j24]Anestis Fachantidis, Matthew E. Taylor, Ioannis P. Vlahavas:
Learning to Teach Reinforcement Learning Agents. Mach. Learn. Knowl. Extr. 1(1): 21-42 (2019) - [j23]Yunshu Du, Assefaw H. Gebremedhin, Matthew E. Taylor:
Analysis of University Fitness Center Data Uncovers Interesting Patterns, Enables Prediction. IEEE Trans. Knowl. Data Eng. 31(8): 1478-1490 (2019) - [c91]Chao Gao, Bilal Kartal, Pablo Hernandez-Leal, Matthew E. Taylor:
On Hard Exploration for Reinforcement Learning: A Case Study in Pommerman. AIIDE 2019: 24-30 - [c90]Pablo Hernandez-Leal, Bilal Kartal, Matthew E. Taylor:
Agent Modeling as Auxiliary Task for Deep Reinforcement Learning. AIIDE 2019: 31-37 - [c89]Bilal Kartal, Pablo Hernandez-Leal, Matthew E. Taylor:
Terminal Prediction as an Auxiliary Task for Deep Reinforcement Learning. AIIDE 2019: 38-44 - [c88]Bilal Kartal, Pablo Hernandez-Leal, Matthew E. Taylor:
Action Guidance with MCTS for Deep Reinforcement Learning. AIIDE 2019: 153-159 - [c87]Weixun Wang, Jianye Hao, Yixi Wang, Matthew E. Taylor:
Achieving cooperation through deep multiagent reinforcement learning in sequential prisoner's dilemmas. DAI 2019: 11:1-11:7 - [c86]Zhaodong Wang, Matthew E. Taylor:
Interactive Reinforcement Learning with Dynamic Reuse of Prior Knowledge from Human and Agent Demonstrations. IJCAI 2019: 3820-3827 - [c85]Kenny Young, Baoxiang Wang, Matthew E. Taylor:
Metatrace Actor-Critic: Online Step-Size Tuning by Meta-gradient Descent for Reinforcement Learning Control. IJCAI 2019: 4185-4191 - [c84]Nathan Douglas, Dianna Yim, Bilal Kartal, Pablo Hernandez-Leal, Frank Maurer, Matthew E. Taylor:
Towers of Saliency: A Reinforcement Learning Visualization Using Immersive Environments. ISS 2019: 339-342 - [e2]Edith Elkind, Manuela Veloso, Noa Agmon, Matthew E. Taylor:
Proceedings of the 18th International Conference on Autonomous Agents and MultiAgent Systems, AAMAS '19, Montreal, QC, Canada, May 13-17, 2019. International Foundation for Autonomous Agents and Multiagent Systems 2019, ISBN 978-1-4503-6309-9 [contents] - [i23]Gabriel Victor de la Cruz, Yunshu Du, Matthew E. Taylor:
Jointly Pre-training with Supervised, Autoencoder, and Value Losses for Deep Reinforcement Learning. CoRR abs/1904.02206 (2019) - [i22]Bilal Kartal, Pablo Hernandez-Leal, Chao Gao, Matthew E. Taylor:
Safer Deep RL with Shallow MCTS: A Case Study in Pommerman. CoRR abs/1904.05759 (2019) - [i21]Chao Gao, Pablo Hernandez-Leal, Bilal Kartal, Matthew E. Taylor:
Skynet: A Top Deep RL Agent in the Inaugural Pommerman Team Competition. CoRR abs/1905.01360 (2019) - [i20]Robert T. Loftin, Bei Peng, Matthew E. Taylor, Michael L. Littman, David L. Roberts:
Interactive Learning of Environment Dynamics for Sequential Tasks. CoRR abs/1907.08478 (2019) - [i19]Pablo Hernandez-Leal, Bilal Kartal, Matthew E. Taylor:
Agent Modeling as Auxiliary Task for Deep Reinforcement Learning. CoRR abs/1907.09597 (2019) - [i18]Bilal Kartal, Pablo Hernandez-Leal, Matthew E. Taylor:
Terminal Prediction as an Auxiliary Task for Deep Reinforcement Learning. CoRR abs/1907.10827 (2019) - [i17]Bilal Kartal, Pablo Hernandez-Leal, Matthew E. Taylor:
Action Guidance with MCTS for Deep Reinforcement Learning. CoRR abs/1907.11703 (2019) - [i16]Chao Gao, Bilal Kartal, Pablo Hernandez-Leal, Matthew E. Taylor:
On Hard Exploration for Reinforcement Learning: a Case Study in Pommerman. CoRR abs/1907.11788 (2019) - 2018
- [j22]Ariel Rosenfeld, Moshe Cohen, Matthew E. Taylor, Sarit Kraus:
Leveraging human knowledge in tabular reinforcement learning: a study of human subjects. Knowl. Eng. Rev. 33: e14 (2018) - [j21]Bei Peng, James MacGlashan, Robert Tyler Loftin, Michael L. Littman, David L. Roberts, Matthew E. Taylor:
Curriculum Design for Machine Learners in Sequential Decision Tasks. IEEE Trans. Emerg. Top. Comput. Intell. 2(4): 268-277 (2018) - [c83]Felipe Leno da Silva, Matthew E. Taylor, Anna Helena Reali Costa:
Autonomously Reusing Knowledge in Multiagent Reinforcement Learning. IJCAI 2018: 5487-5493 - [c82]Matthew E. Taylor:
Improving Reinforcement Learning with Human Input. IJCAI 2018: 5724-5728 - [i15]Weixun Wang, Jianye Hao, Yixi Wang, Matthew E. Taylor:
Towards Cooperation in Sequential Prisoner's Dilemmas: a Deep Multiagent Reinforcement Learning Approach. CoRR abs/1803.00162 (2018) - [i14]Zhaodong Wang, Matthew E. Taylor:
Interactive Reinforcement Learning with Dynamic Reuse of Prior Knowledge from Human/Agent's Demonstration. CoRR abs/1805.04493 (2018) - [i13]Kenny Young, Baoxiang Wang, Matthew E. Taylor:
Metatrace: Online Step-size Tuning by Meta-gradient Descent for Reinforcement Learning Control. CoRR abs/1805.04514 (2018) - [i12]Ariel Rosenfeld, Moshe Cohen, Matthew E. Taylor, Sarit Kraus:
Leveraging human knowledge in tabular reinforcement learning: A study of human subjects. CoRR abs/1805.05769 (2018) - [i11]Pablo Hernandez-Leal, Bilal Kartal, Matthew E. Taylor:
Is multiagent deep reinforcement learning the answer or the question? A brief survey. CoRR abs/1810.05587 (2018) - [i10]Behzad Ghazanfari, Fatemeh Afghah, Matthew E. Taylor:
Autonomous Extraction of a Hierarchical Structure of Tasks in Reinforcement Learning, A Sequential Associate Rule Mining Approach. CoRR abs/1811.08275 (2018) - [i9]Bilal Kartal, Pablo Hernandez-Leal, Matthew E. Taylor:
Using Monte Carlo Tree Search as a Demonstrator within Asynchronous Deep RL. CoRR abs/1812.00045 (2018) - [i8]Gabriel Victor de la Cruz, Yunshu Du, Matthew E. Taylor:
Pre-training with Non-expert Human Demonstration for Deep Reinforcement Learning. CoRR abs/1812.08904 (2018) - 2017
- [j20]Pablo Hernandez-Leal, Yusen Zhan, Matthew E. Taylor, Luis Enrique Sucar, Enrique Munoz de Cote:
Efficiently detecting switches against non-stationary opponents. Auton. Agents Multi Agent Syst. 31(4): 767-789 (2017) - [j19]Pablo Hernandez-Leal, Yusen Zhan, Matthew E. Taylor, Luis Enrique Sucar, Enrique Munoz de Cote:
An exploration strategy for non-stationary opponents. Auton. Agents Multi Agent Syst. 31(5): 971-1002 (2017) - [j18]Tim Brys, Anna Harutyunyan, Peter Vrancx, Ann Nowé, Matthew E. Taylor:
Multi-objectivization and ensembles of shapings in reinforcement learning. Neurocomputing 263: 48-59 (2017) - [j17]Yusen Zhan, Haitham Bou-Ammar, Matthew E. Taylor:
Nonconvex Policy Search Using Variational Inequalities. Neural Comput. 29(10): 2800-2824 (2017) - [j16]Yusen Zhan, Haitham Bou-Ammar, Matthew E. Taylor:
Scalable lifelong reinforcement learning. Pattern Recognit. 72: 407-418 (2017) - [j15]Yunxiang Ye, Zhaodong Wang, Dylan Jones, Long He, Matthew E. Taylor, Geoffrey A. Hollinger, Qin Zhang:
Bin-Dog: A Robotic Platform for Bin Management in Orchards. Robotics 6(2): 12 (2017) - [c81]Salam El Bsat, Haitham Bou-Ammar, Matthew E. Taylor:
Scalable Multitask Policy Gradient Reinforcement Learning. AAAI 2017: 1847-1853 - [c80]Matthew E. Taylor, Sakire Arslan Ay:
AI Projects for Computer Science Capstone Classes (Extended Abstract). AAAI 2017: 4819-4821 - [c79]Amanda Leah Zulas, Kaitlyn I. Franz, Darrin Griechen, Matthew E. Taylor:
Solar Decathlon Competition: Towards a Solar-Powered Smart Home. AAAI Workshops 2017 - [c78]Pablo Hernandez-Leal, Yusen Zhan, Matthew E. Taylor, Luis Enrique Sucar, Enrique Munoz de Cote:
Detecting Switches Against Non-Stationary Opponents. AAMAS 2017: 920-921 - [c77]Pablo Hernandez-Leal, Yusen Zhan, Matthew E. Taylor, Luis Enrique Sucar, Enrique Munoz de Cote:
An Exploration Strategy Facing Non-Stationary Agents. AAMAS 2017: 922-923 - [c76]Bei Peng, James MacGlashan, Robert T. Loftin, Michael L. Littman, David L. Roberts, Matthew E. Taylor:
Curriculum Design for Machine Learners in Sequential Decision Tasks. AAMAS 2017: 1682-1684 - [c75]Ariel Rosenfeld, Matthew E. Taylor, Sarit Kraus:
Speeding up Tabular Reinforcement Learning Using State-Action Similarities. AAMAS 2017: 1722-1724 - [c74]James MacGlashan, Mark K. Ho, Robert Tyler Loftin, Bei Peng, Guan Wang, David L. Roberts, Matthew E. Taylor, Michael L. Littman:
Interactive Learning from Policy-Dependent Human Feedback. ICML 2017: 2285-2294 - [c73]Zhaodong Wang, Matthew E. Taylor:
Improving Reinforcement Learning with Confidence-Based Demonstrations. IJCAI 2017: 3027-3033 - [c72]Ariel Rosenfeld, Matthew E. Taylor, Sarit Kraus:
Leveraging Human Knowledge in Tabular Reinforcement Learning: A Study of Human Subjects. IJCAI 2017: 3823-3830 - [i7]James MacGlashan, Mark K. Ho, Robert Tyler Loftin, Bei Peng, David L. Roberts, Matthew E. Taylor, Michael L. Littman:
Interactive Learning from Policy-Dependent Human Feedback. CoRR abs/1701.06049 (2017) - [i6]Anestis Fachantidis, Matthew E. Taylor, Ioannis P. Vlahavas:
Learning to Teach Reinforcement Learning Agents. CoRR abs/1707.09079 (2017) - [i5]Gabriel Victor de la Cruz, Yunshu Du, Matthew E. Taylor:
Pre-training Neural Networks with Human Demonstrations for Deep Reinforcement Learning. CoRR abs/1709.04083 (2017) - [i4]Behzad Ghazanfari, Matthew E. Taylor:
Autonomous Extracting a Hierarchical Structure of Tasks in Reinforcement Learning and Multi-task Reinforcement Learning. CoRR abs/1709.04579 (2017) - 2016
- [j14]Robert T. Loftin, Bei Peng, James MacGlashan, Michael L. Littman, Matthew E. Taylor, Jeff Huang, David L. Roberts:
Learning behaviors via human-delivered discrete feedback: modeling implicit feedback strategies to speed up learning. Auton. Agents Multi Agent Syst. 30(1): 30-59 (2016) - [c71]Pablo Hernandez-Leal, Matthew E. Taylor, Benjamin Rosman, Luis Enrique Sucar, Enrique Munoz de Cote:
Identifying and Tracking Switching, Non-Stationary Opponents: A Bayesian Approach. AAAI Workshop: Multiagent Interaction without Prior Coordination 2016 - [c70]William Curran, Tim Brys, David W. Aha, Matthew E. Taylor, William D. Smart:
Dimensionality Reduced Reinforcement Learning for Assistive Robots. AAAI Fall Symposia 2016 - [c69]Robert Tyler Loftin, James MacGlashan, Bei Peng, Matthew E. Taylor, Michael L. Littman, David L. Roberts:
Towards Behavior-Aware Model Learning from Human-Generated Trajectories. AAAI Fall Symposia 2016 - [c68]Zhaodong Wang, Matthew E. Taylor:
Effective Transfer via Demonstrations in Reinforcement Learning: A Preliminary Study.