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2020 – today
- 2024
[j45]Atefeh Shahroudnejad, Payam Mousavi, Oleksii Perepelytsia, Sahir, David Staszak, Matthew E. Taylor, Brent Bawel:
A novel framework for automated warehouse layout generation. Frontiers Artif. Intell. 7 (2024)- [j44]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: 359-415 (2024) - [j43]Brittany Davis Pierson, Dustin Arendt, John Miller, Matthew E. Taylor:
Comparing explanations in RL. Neural Comput. Appl. 36(1): 505-516 (2024) - [j42]Upma Gandhi, Erfan Aghaeekiasaraee, Sahir, Payam Mousavi, Ismail S. K. Bustany, Matthew E. Taylor, Laleh Behjat:
Applying reinforcement learning to learn best net to rip and re-route in global routing. ACM Trans. Design Autom. Electr. Syst. 29(4): 1-21 (2024) - [c132]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: 15934-15942 - [c131]Tianpei Yang, Heng You, Jianye Hao, Yan Zheng, Matthew E. Taylor:
A Transfer Approach Using Graph Neural Networks in Deep Reinforcement Learning. AAAI 2024: 16352-16360 - [c130]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: 733-742 - [c129]Simone Parisi, Montaser Mohammedalamen, Alireza Kazemipour, Matthew E. Taylor, Michael Bowling:
Monitored Markov Decision Processes. AAMAS 2024: 1549-1557 - [c128]Chaitanya Kharyal, Sai Krishna Gottipati, Tanmay Kumar Sinha, Srijita Das, Matthew E. Taylor:
GLIDE-RL: Grounded Language Instruction through DEmonstration in RL. AAMAS 2024: 2333-2335 - [c127]Calarina Muslimani, Matthew E. Taylor:
Leveraging Sub-Optimal Data for Human-in-the-Loop Reinforcement Learning. AAMAS 2024: 2399-2401 - [c126]Hao Zhang, Tianpei Yang, Yan Zheng, Jianye Hao, Matthew E. Taylor:
PADDLE: Logic Program Guided Policy Reuse in Deep Reinforcement Learning. AAMAS 2024: 2585-2587 - [c125]Raechel Walker, Olivia Dias, Matthew E. Taylor, Cynthia Breazeal:
Alleviating the Danger Of A Single Story Through Liberatory Computing Education. RESPECT 2024: 169-178 - [i66]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 abs/2401.00907 (2024) - [i65]Chaitanya Kharyal, Sai Krishna Gottipati, Tanmay Kumar Sinha, Srijita Das, Matthew E. Taylor:
GLIDE-RL: Grounded Language Instruction through DEmonstration in RL. CoRR abs/2401.02991 (2024) - [i64]Simone Parisi, Montaser Mohammedalamen, Alireza Kazemipour, Matthew E. Taylor, Michael Bowling:
Monitored Markov Decision Processes. CoRR abs/2402.06819 (2024) - [i63]Shang Wang, Deepak Ranganatha Sastry Mamillapalli, Tianpei Yang, Matthew E. Taylor:
FPGA Divide-and-Conquer Placement using Deep Reinforcement Learning. CoRR abs/2404.13061 (2024) - [i62]Calarina Muslimani, Matthew E. Taylor:
Leveraging Sub-Optimal Data for Human-in-the-Loop Reinforcement Learning. CoRR abs/2405.00746 (2024) - [i61]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 abs/2406.06495 (2024) - [i60]Atefeh Shahroudnejad, Payam Mousavi, Oleksii Perepelytsia, Sahir, David Staszak, Matthew E. Taylor, Brent Bawel:
A Novel Framework for Automated Warehouse Layout Generation. CoRR abs/2407.08633 (2024) - [i59]Manan Tomar, Philippe Hansen-Estruch, Philip Bachman, Alex Lamb, John Langford, Matthew E. Taylor, Sergey Levine:
Video Occupancy Models. CoRR abs/2407.09533 (2024) - [i58]Matan Shamir, Osher Elhadad, Matthew E. Taylor, Reuth Mirsky:
ODGR: Online Dynamic Goal Recognition. CoRR abs/2407.16220 (2024) - [i57]Yuxuan Li, Srijita Das, Matthew E. Taylor:
CANDERE-COACH: Reinforcement Learning from Noisy Feedback. CoRR abs/2409.15521 (2024) - [i56]Michael Przystupa, Gauthier Gidel, Matthew E. Taylor, Martin Jägersand, Justus Piater, Samuele Tosatto:
Investigating the Benefits of Nonlinear Action Maps in Data-Driven Teleoperation. CoRR abs/2410.21406 (2024) - 2023
- [j41]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): 45 (2023) - [j40]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) - [j39]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): 23429-23439 (2023) - [j38]Calarina Muslimani, Alex Lewandowski, Dale Schuurmans, Matthew E. Taylor, Jun Luo:
Reinforcement Teaching. Trans. Mach. Learn. Res. 2023 (2023) - [j37]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) - [j36]Su Zhang, Srijita Das, Sriram Ganapathi Subramanian, Matthew E. Taylor:
Two-Level Actor-Critic Using Multiple Teachers. Trans. Mach. Learn. Res. 2023 (2023) - [c124]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: 11604-11612 - [c123]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:
Model AI Assignments 2023. AAAI 2023: 16104-16105 - [c122]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: 16437-16439 - [c121]Calarina Muslimani, Saba Gul, Matthew E. Taylor, Carrie Demmans Epp, Christabel Wayllace:
C2Tutor: Helping People Learn to Avoid Present Bias During Decision Making. AIED 2023: 733-738 - [c120]Sriram Ganapathi Subramanian, Matthew E. Taylor, Kate Larson, Mark Crowley:
Learning from Multiple Independent Advisors in Multi-agent Reinforcement Learning. AAMAS 2023: 1144-1153 - [c119]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 - [c118]Chaitanya Kharyal, Tanmay Kumar 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 - [c117]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 - [c116]Su Zhang, Srijita Das, Sriram Ganapathi Subramanian, Matthew E. Taylor:
Two-Level Actor-Critic Using Multiple Teachers. AAMAS 2023: 2589-2591 - [c115]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:
Multi-Robot Warehouse Optimization: Leveraging Machine Learning for Improved Performance. AAMAS 2023: 3047-3049 - [c114]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 - [c113]Xiaoxue Du, Sharifa Alghowinem, Matthew E. Taylor, Kate Darling, Cynthia Breazeal:
Innovating AI Leadership Education. FIE 2023: 1-8 - [c112]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 - [c111]Matthew E. Taylor:
Reinforcement Learning Requires Human-in-the-Loop Framing and Approaches. HHAI 2023: 351-360 - [c110]Fatemeh Abdollahi, Saqib Ameen, Matthew E. Taylor, Levi H. S. Lelis:
Can You Improve My Code? Optimizing Programs with Local Search. IJCAI 2023: 2940-2948 - [c109]Sriram Ganapathi Subramanian, Matthew E. Taylor, Kate Larson, Mark Crowley:
Multi-Agent Advisor Q-Learning (Extended Abstract). IJCAI 2023: 6884-6889 - [c108]Manan Tomar, Riashat Islam, Matthew E. Taylor, Sergey Levine, Philip Bachman:
Ignorance is Bliss: Robust Control via Information Gating. NeurIPS 2023 - [i55]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) - [i54]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) - [i53]Fatemeh Abdollahi, Saqib Ameen, Matthew E. Taylor, Levi H. S. Lelis:
Can You Improve My Code? Optimizing Programs with Local Search. CoRR abs/2307.05603 (2023) - [i52]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 abs/2311.00810 (2023) - [i51]Laila El Moujtahid, Sai Krishna Gottipati, Clodéric Mars, Matthew E. Taylor:
Human-Machine Teaming for UAVs: An Experimentation Platform. CoRR abs/2312.11718 (2023) - [i50]Rupali Bhati, Sai Krishna Gottipati, Clodéric Mars, Matthew E. Taylor:
Curriculum Learning for Cooperation in Multi-Agent Reinforcement Learning. CoRR abs/2312.11768 (2023) - [i49]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 abs/2312.15160 (2023) - [i48]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 abs/2312.15339 (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) - [c107]Sriram Ganapathi Subramanian, Matthew E. Taylor, Mark Crowley, Pascal Poupart:
Decentralized Mean Field Games. AAAI 2022: 9439-9447 - [c106]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 - [c105]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 - [c104]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 - [c103]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
- [c102]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 - [c101]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 - [c100]Sriram Ganapathi Subramanian, Matthew E. Taylor, Mark Crowley, Pascal Poupart:
Partially Observable Mean Field Reinforcement Learning. AAMAS 2021: 537-545 - [c99]Matthew E. Taylor:
Reinforcement Learning for Electronic Design Automation: Successes and Opportunities. ISPD 2021: 3 - [c98]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: 73-84 - [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) - [c97]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 - [c96]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 - [c95]Sriram Ganapathi Subramanian, Pascal Poupart, Matthew E. Taylor, Nidhi Hegde:
Multi Type Mean Field Reinforcement Learning. AAMAS 2020: 411-419 - [c94]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) - [c93]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 - [c92]Pablo Hernandez-Leal, Bilal Kartal, Matthew E. Taylor:
Agent Modeling as Auxiliary Task for Deep Reinforcement Learning. AIIDE 2019: 31-37 - [c91]Bilal Kartal, Pablo Hernandez-Leal, Matthew E. Taylor:
Terminal Prediction as an Auxiliary Task for Deep Reinforcement Learning. AIIDE 2019: 38-44 - [c90]Bilal Kartal, Pablo Hernandez-Leal, Matthew E. Taylor:
Action Guidance with MCTS for Deep Reinforcement Learning. AIIDE 2019: 153-159 - [c89]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 - [c88]