A GPU-Based Accelerator for Chinese Word Segmentation. - Xiwu Gu; Ruixuan Li 001; Kunmei Wen; Bei Peng 001; Weijun Xiao (APWeb, 2012)

A Strategy-Aware Technique for Learning Behaviors from Discrete Human Feedback. - Robert Tyler Loftin; James MacGlashan; Bei Peng 001; Matthew E. Taylor; Michael L. Littman; Jeff Huang 002; David L. Roberts 001 (AAAI, 2014)

Learning something from nothing: Leveraging implicit human feedback strategies. - Robert Tyler Loftin; Bei Peng 001; James MacGlashan; Michael L. Littman; Matthew E. Taylor; Jeff Huang 002; David L. Roberts 001 (RO-MAN, 2014)

Generating Real-Time Crowd Advice to Improve Reinforcement Learning Agents. - Gabriel Victor de la Cruz; Bei Peng 001; Walter Stephen Lasecki; Matthew Edmund Taylor (AAAI Workshop: Learning for General Competency in Video Games, 2015)

On the Ability to Provide Demonstrations on a UAS: Observing 90 Untrained Participants Abusing a Flying Robot. - Mitchell Scott; Bei Peng 001; Madeline Chili; Tanay Nigam; Francis G. Pascual; Cynthia Matuszek; Matthew E. Taylor (AAAI Fall Symposia, 2015)

Towards Integrating Real-Time Crowd Advice with Reinforcement Learning. - Gabriel Victor de la Cruz; Bei Peng 001; Walter S. Lasecki; Matthew E. Taylor (IUI Companion, 2015)

Towards Behavior-Aware Model Learning from Human-Generated Trajectories. - Robert Tyler Loftin; James MacGlashan; Bei Peng 001; Matthew E. Taylor; Michael L. Littman; David L. Roberts 001 (AAAI Fall Symposia, 2016)

A Need for Speed: Adapting Agent Action Speed to Improve Task Learning from Non-Expert Humans. - Bei Peng 001; James MacGlashan; Robert Tyler Loftin; Michael L. Littman; David L. Roberts 001; Matthew E. Taylor (AAMAS, 2016)

Learning behaviors via human-delivered discrete feedback: modeling implicit feedback strategies to speed up learning. - Robert T. Loftin; Bei Peng 001; James MacGlashan; Michael L. Littman; Matthew E. Taylor; Jeff Huang 002; David L. Roberts 001 (Auton. Agents Multi Agent Syst., 2016)

How Do Humans Teach: On Curriculum Design for Machine Learners. - Bei Peng 001 (AAMAS, 2017)

Interactive Learning from Policy-Dependent Human Feedback. - James MacGlashan; Mark K. Ho; Robert Tyler Loftin; Bei Peng 001; Guan Wang; David L. Roberts 001; Matthew E. Taylor; Michael L. Littman (ICML, 2017)

Interactive Learning from Policy-Dependent Human Feedback. - James MacGlashan; Mark K. Ho; Robert Tyler Loftin; Bei Peng 001; David L. Roberts 001; Matthew E. Taylor; Michael L. Littman (CoRR, 2017)

Curriculum Design for Machine Learners in Sequential Decision Tasks. - Bei Peng 001; James MacGlashan; Robert Tyler Loftin; Michael L. Littman; David L. Roberts 001; Matthew E. Taylor (IEEE Trans. Emerg. Top. Comput. Intell., 2018)

Interactive Learning of Environment Dynamics for Sequential Tasks. - Robert T. Loftin; Bei Peng 001; Matthew E. Taylor; Michael L. Littman; David L. Roberts 001 (CoRR, 2019)

VIABLE: Fast Adaptation via Backpropagating Learned Loss. - Leo Feng; Luisa M. Zintgraf; Bei Peng 001; Shimon Whiteson (CoRR, 2019)

Optimistic Exploration even with a Pessimistic Initialisation. - Tabish Rashid; Bei Peng 001; Wendelin Boehmer; Shimon Whiteson (ICLR, 2020)

Weighted QMIX: Expanding Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning. - Tabish Rashid; Gregory Farquhar; Bei Peng 001; Shimon Whiteson (NeurIPS, 2020)

Deep Multi-Agent Reinforcement Learning for Decentralized Continuous Cooperative Control. - Christian Schröder de Witt; Bei Peng 001; Pierre-Alexandre Kamienny; Philip H. S. Torr; Wendelin Böhmer; Shimon Whiteson (CoRR, 2020)

AI-QMIX: Attention and Imagination for Dynamic Multi-Agent Reinforcement Learning. - Shariq Iqbal; Christian A. Schröder de Witt; Bei Peng 001; Wendelin Böhmer; Shimon Whiteson; Fei Sha (CoRR, 2020)

Weighted QMIX: Expanding Monotonic Value Function Factorisation. - Tabish Rashid; Gregory Farquhar; Bei Peng 001; Shimon Whiteson (CoRR, 2020)

Curriculum Learning for Reinforcement Learning Domains: A Framework and Survey. - Sanmit Narvekar; Bei Peng 001; Matteo Leonetti; Jivko Sinapov; Matthew E. Taylor; Peter Stone 001 (J. Mach. Learn. Res., 2020)

Special issue on adaptive and learning agents 2019. - Patrick Mannion; Patrick MacAlpine; Bei Peng 001; Roxana Radulescu (Knowl. Eng. Rev., 2020)

RODE: Learning Roles to Decompose Multi-Agent Tasks. - Tonghan Wang 001; Tarun Gupta 002; Anuj Mahajan; Bei Peng 001; Shimon Whiteson; Chongjie Zhang (ICLR, 2021)

UneVEn: Universal Value Exploration for Multi-Agent Reinforcement Learning. - Tarun Gupta 002; Anuj Mahajan; Bei Peng 001; Wendelin Boehmer; Shimon Whiteson (ICML, 2021)

Randomized Entity-wise Factorization for Multi-Agent Reinforcement Learning. - Shariq Iqbal; Christian A. Schröder de Witt; Bei Peng 001; Wendelin Boehmer; Shimon Whiteson; Fei Sha (ICML, 2021)

Regularized Softmax Deep Multi-Agent Q-Learning. - Ling Pan; Tabish Rashid; Bei Peng 001; Longbo Huang; Shimon Whiteson (NeurIPS, 2021)

FACMAC: Factored Multi-Agent Centralised Policy Gradients. - Bei Peng 001; Tabish Rashid; Christian Schröder de Witt; Pierre-Alexandre Kamienny; Philip H. S. Torr; Wendelin Boehmer; Shimon Whiteson (NeurIPS, 2021)

Softmax with Regularization: Better Value Estimation in Multi-Agent Reinforcement Learning. - Ling Pan; Tabish Rashid; Bei Peng 001; Longbo Huang; Shimon Whiteson (CoRR, 2021)

Semi-On-Policy Training for Sample Efficient Multi-Agent Policy Gradients. - Bozhidar Vasilev; Tarun Gupta 002; Bei Peng 001; Shimon Whiteson (CoRR, 2021)

Special issue on adaptive and learning agents 2018. - Patrick Mannion; Anna Harutyunyan; Bei Peng 001; Kaushik Subramanian (Knowl. Eng. Rev., 2021)

Dependable learning-enabled multiagent systems. - Xiaowei Huang 001; Bei Peng 001; Xingyu Zhao 001 (AI Commun., 2022)

Curriculum Learning for Relative Overgeneralization. - Lin Shi; Bei Peng 001 (CoRR, 2022)

Deep Reinforcement Learning for Continuous Control of Material Thickness. - Oliver Dippel; Alexei Lisitsa 001; Bei Peng 001 (SGAI Conf., 2023)

Accelerating Laboratory Automation Through Robot Skill Learning For Sample Scraping. - Gabriella Pizzuto; Hetong Wang; Hatem Fakhruldeen; Bei Peng 001; Kevin S. Luck; Andrew I. Cooper (CASE, 2024)

Contextual Transformers for Goal-Oriented Reinforcement Learning. - Oliver Dippel; Alexei Lisitsa 001; Bei Peng 001 (SGAI Conf. (1), 2024)

SIDA: Social Media Image Deepfake Detection, Localization and Explanation with Large Multimodal Model. - Zhenglin Huang; Jinwei Hu; Xiangtai Li; Yiwei He; Xingyu Zhao 001; Bei Peng 001; Baoyuan Wu; Xiaowei Huang 001; Guangliang Cheng (CVPR, 2025)

So-Fake: Benchmarking and Explaining Social Media Image Forgery Detection. - Zhenglin Huang; Tianxiao Li; Xiangtai Li; Haiquan Wen; Yiwei He; Jiangning Zhang; Hao Fei 001; Xi Yang 008; Xiaowei Huang 001; Bei Peng 001; Guangliang Cheng (CoRR, 2025)