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ORCIDBei Peng 0001
Person information
- affiliation: University of Liverpool, UK
- affiliation (former): University of Oxford, UK
- affiliation (PhD 2018): Washington State University, Pullman, WA, USA
Other persons with the same name
- Bei Peng — disambiguation page
- Bei Peng 0002
![[0000-0003-2486-6776]](https://dblp.org/img/orcid-mark.12x12.png "0000-0003-2486-6776")
— University of Electronic Science and Technology of China, School of Mechanical and Electrical Engineering, Chengdu, China (and 1 more)
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2020 – today
- 2024
[c20]Gabriella Pizzuto, Hetong Wang, Hatem Fakhruldeen, Bei Peng, Kevin S. Luck, Andrew I. Cooper:
Accelerating Laboratory Automation Through Robot Skill Learning For Sample Scraping. CASE 2024: 2103-2110- 2023
- [c19]Oliver Dippel, Alexei Lisitsa, Bei Peng:
Deep Reinforcement Learning for Continuous Control of Material Thickness. SGAI Conf. 2023: 321-334 - 2022
- [j6]Xiaowei Huang, Bei Peng, Xingyu Zhao:
Dependable learning-enabled multiagent systems. AI Commun. 35(4): 407-420 (2022) - [i14]Gabriella Pizzuto, Hetong Wang, Hatem Fakhruldeen, Bei Peng, Kevin S. Luck, Andrew I. Cooper:
Accelerating Laboratory Automation Through Robot Skill Learning For Sample Scraping. CoRR abs/2209.14875 (2022) - [i13]Lin Shi, Bei Peng:
Curriculum Learning for Relative Overgeneralization. CoRR abs/2212.02733 (2022) - 2021
- [j5]Patrick Mannion, Anna Harutyunyan, Bei Peng, Kaushik Subramanian:
Special issue on adaptive and learning agents 2018. Knowl. Eng. Rev. 36: e7 (2021) - [c18]Tonghan Wang, Tarun Gupta, Anuj Mahajan, Bei Peng, Shimon Whiteson, Chongjie Zhang:
RODE: Learning Roles to Decompose Multi-Agent Tasks. ICLR 2021 - [c17]Tarun Gupta, Anuj Mahajan, Bei Peng, Wendelin Boehmer, Shimon Whiteson:
UneVEn: Universal Value Exploration for Multi-Agent Reinforcement Learning. ICML 2021: 3930-3941 - [c16]Shariq Iqbal, Christian A. Schröder de Witt, Bei Peng, Wendelin Boehmer, Shimon Whiteson, Fei Sha:
Randomized Entity-wise Factorization for Multi-Agent Reinforcement Learning. ICML 2021: 4596-4606 - [c15]Ling Pan, Tabish Rashid, Bei Peng, Longbo Huang, Shimon Whiteson:
Regularized Softmax Deep Multi-Agent Q-Learning. NeurIPS 2021: 1365-1377 - [c14]Bei Peng, Tabish Rashid, Christian Schröder de Witt, Pierre-Alexandre Kamienny, Philip H. S. Torr, Wendelin Boehmer, Shimon Whiteson:
FACMAC: Factored Multi-Agent Centralised Policy Gradients. NeurIPS 2021: 12208-12221 - [i12]Ling Pan, Tabish Rashid, Bei Peng, Longbo Huang, Shimon Whiteson:
Softmax with Regularization: Better Value Estimation in Multi-Agent Reinforcement Learning. CoRR abs/2103.11883 (2021) - [i11]Bozhidar Vasilev, Tarun Gupta, Bei Peng, Shimon Whiteson:
Semi-On-Policy Training for Sample Efficient Multi-Agent Policy Gradients. CoRR abs/2104.13446 (2021) - 2020
- [j4]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) - [j3]Patrick Mannion, Patrick MacAlpine, Bei Peng, Roxana Radulescu:
Special issue on adaptive and learning agents 2019. Knowl. Eng. Rev. 35: e18 (2020) - [c13]Tabish Rashid, Bei Peng, Wendelin Boehmer, Shimon Whiteson:
Optimistic Exploration even with a Pessimistic Initialisation. ICLR 2020 - [c12]Tabish Rashid, Gregory Farquhar, Bei Peng, Shimon Whiteson:
Weighted QMIX: Expanding Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning. NeurIPS 2020 - [i10]Tabish Rashid, Bei Peng, Wendelin Böhmer, Shimon Whiteson:
Optimistic Exploration even with a Pessimistic Initialisation. CoRR abs/2002.12174 (2020) - [i9]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) - [i8]Christian Schröder de Witt, Bei Peng, Pierre-Alexandre Kamienny, Philip H. S. Torr, Wendelin Böhmer, Shimon Whiteson:
Deep Multi-Agent Reinforcement Learning for Decentralized Continuous Cooperative Control. CoRR abs/2003.06709 (2020) - [i7]Shariq Iqbal, Christian A. Schröder de Witt, Bei Peng, Wendelin Böhmer, Shimon Whiteson, Fei Sha:
AI-QMIX: Attention and Imagination for Dynamic Multi-Agent Reinforcement Learning. CoRR abs/2006.04222 (2020) - [i6]Tabish Rashid, Gregory Farquhar, Bei Peng, Shimon Whiteson:
Weighted QMIX: Expanding Monotonic Value Function Factorisation. CoRR abs/2006.10800 (2020) - [i5]Tonghan Wang, Tarun Gupta, Anuj Mahajan, Bei Peng, Shimon Whiteson, Chongjie Zhang:
RODE: Learning Roles to Decompose Multi-Agent Tasks. CoRR abs/2010.01523 (2020) - [i4]Tarun Gupta, Anuj Mahajan, Bei Peng, Wendelin Böhmer, Shimon Whiteson:
UneVEn: Universal Value Exploration for Multi-Agent Reinforcement Learning. CoRR abs/2010.02974 (2020)
2010 – 2019
- 2019
- [i3]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) - [i2]Leo Feng, Luisa M. Zintgraf, Bei Peng, Shimon Whiteson:
VIABLE: Fast Adaptation via Backpropagating Learned Loss. CoRR abs/1911.13159 (2019) - 2018
- [j2]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) - 2017
- [c11]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 - [c10]Bei Peng:
How Do Humans Teach: On Curriculum Design for Machine Learners. AAMAS 2017: 1851-1852 - [c9]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 - [i1]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) - 2016
- [j1]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) - [c8]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 - [c7]Bei Peng, James MacGlashan, Robert Tyler Loftin, Michael L. Littman, David L. Roberts, Matthew E. Taylor:
A Need for Speed: Adapting Agent Action Speed to Improve Task Learning from Non-Expert Humans. AAMAS 2016: 957-965 - 2015
- [c6]Gabriel Victor de la Cruz, Bei Peng, Walter Stephen Lasecki, Matthew Edmund Taylor:
Generating Real-Time Crowd Advice to Improve Reinforcement Learning Agents. AAAI Workshop: Learning for General Competency in Video Games 2015 - [c5]Mitchell Scott, Bei Peng, Madeline Chili, Tanay Nigam, Francis G. Pascual, Cynthia Matuszek, Matthew E. Taylor:
On the Ability to Provide Demonstrations on a UAS: Observing 90 Untrained Participants Abusing a Flying Robot. AAAI Fall Symposia 2015: 117-121 - [c4]Gabriel Victor de la Cruz, Bei Peng, Walter S. Lasecki, Matthew E. Taylor:
Towards Integrating Real-Time Crowd Advice with Reinforcement Learning. IUI Companion 2015: 17-20 - 2014
- [c3]Robert Tyler Loftin, James MacGlashan, Bei Peng, Matthew E. Taylor, Michael L. Littman, Jeff Huang, David L. Roberts:
A Strategy-Aware Technique for Learning Behaviors from Discrete Human Feedback. AAAI 2014: 937-943 - [c2]Robert Tyler Loftin, Bei Peng, James MacGlashan, Michael L. Littman, Matthew E. Taylor, Jeff Huang, David L. Roberts:
Learning something from nothing: Leveraging implicit human feedback strategies. RO-MAN 2014: 607-612 - 2012
- [c1]Xiwu Gu, Ruixuan Li, Kunmei Wen, Bei Peng, Weijun Xiao:
A GPU-Based Accelerator for Chinese Word Segmentation. APWeb 2012: 231-242
Coauthor Index
aka: Matthew Edmund Taylor
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