| Curriculum Learning for Reinforcement Learning Domains: A Framework and Survey S Narvekar, B Peng, M Leonetti, J Sinapov, ME Taylor, P Stone Journal of Machine Learning Research (JMLR 2020) 21, 1-50, 2020 | 870 | 2020 |
| Weighted QMIX: Expanding Monotonic Value Function Factorisation T Rashid, G Farquhar, B Peng, S Whiteson Thirty-fourth Conference on Neural Information Processing Systems (NeurIPS 2020), 2020 | 561* | 2020 |
| Interactive learning from policy-dependent human feedback J MacGlashan, MK Ho, R Loftin, B Peng, G Wang, DL Roberts, ME Taylor, ... 34th International Conference on Machine Learning (ICML 2017), 2285-2294, 2017 | 433 | 2017 |
| FACMAC: Factored Multi-Agent Centralised Policy Gradients B Peng, T Rashid, CAS de Witt, PA Kamienny, PHS Torr, W Böhmer, ... 35th Conference on Neural Information Processing Systems (NeurIPS 2021), 2021 | 401 | 2021 |
| RODE: Learning Roles to Decompose Multi-Agent Tasks T Wang, T Gupta, A Mahajan, B Peng, S Whiteson, C Zhang International Conference on Learning Representations (ICLR 2021), 2020 | 322 | 2020 |
| Learning behaviors via human-delivered discrete feedback: modeling implicit feedback strategies to speed up learning R Loftin, B Peng, J MacGlashan, ML Littman, ME Taylor, J Huang, ... Autonomous agents and multi-agent systems (JAAMAS 2016) 30 (1), 30-59, 2016 | 143 | 2016 |
| Randomized Entity-wise Factorization for Multi-Agent Reinforcement Learning S Iqbal, CAS de Witt, B Peng, W Böhmer, S Whiteson, F Sha 38th International Conference on Machine Learning (ICML 2021), 2021 | 137* | 2021 |
| Deep Multi-Agent Reinforcement Learning for Decentralized Continuous Cooperative Control CS de Witt, B Peng (equal contribution), PA Kamienny, P Torr, W Böhmer, ... arXiv preprint arXiv:2003.06709, 2020 | 118 | 2020 |
| A strategy-aware technique for learning behaviors from discrete human feedback RT Loftin, J MacGlashan, B Peng, ME Taylor, ML Littman, J Huang, ... Twenty-Eighth AAAI Conference on Artificial Intelligence (AAAI 2014), 2014 | 88 | 2014 |
| UneVEn: Universal Value Exploration for Multi-Agent Reinforcement Learning T Gupta, A Mahajan, B Peng, W Böhmer, S Whiteson 38th International Conference on Machine Learning (ICML 2021), 2021 | 69 | 2021 |
| A need for speed: Adapting agent action speed to improve task learning from non-expert humans B Peng, J MacGlashan, R Loftin, ML Littman, DL Roberts, ME Taylor Autonomous Agents and Multiagent Systems (AAMAS 2016), 2016 | 63 | 2016 |
| Optimistic Exploration even with a Pessimistic Initialisation T Rashid, B Peng, W Böhmer, S Whiteson International Conference on Learning Representations (ICLR 2020), 2020 | 61 | 2020 |
| Regularized Softmax Deep Multi-Agent Q-Learning L Pan, T Rashid, B Peng, L Huang, S Whiteson 35th Conference on Neural Information Processing Systems (NeurIPS 2021), 2021 | 55* | 2021 |
| Learning something from nothing: Leveraging implicit human feedback strategies R Loftin, B Peng, J MacGlashan, ML Littman, ME Taylor, J Huang, ... The 23rd IEEE international symposium on robot and human interactive …, 2014 | 37 | 2014 |
| Training an agent to ground commands with reward and punishment J MacGlashan, M Littman, R Loftin, B Peng, D Roberts, ME Taylor Proceedings of the AAAI Machine Learning for Interactive Systems Workshop, 6-12, 2014 | 26 | 2014 |
| Curriculum Design for Machine Learners in Sequential Decision Tasks B Peng, J MacGlashan, R Loftin, ML Littman, DL Roberts, ME Taylor IEEE Transactions on Emerging Topics in Computational Intelligence 2 (4 …, 2018 | 25 | 2018 |
| Accelerating laboratory automation through robot skill learning for sample scraping G Pizzuto, H Wang, H Fakhruldeen, B Peng, KS Luck, AI Cooper 2024 IEEE 20th International Conference on Automation Science and …, 2024 | 21 | 2024 |
| Convergent Actor Critic by Humans J MacGlashan, ML Littman, DL Roberts, R Loftin, B Peng, ME Taylor International Conference on Intelligent Robots and Systems (IROS 2016), 2016 | 17 | 2016 |
| Improving diversity of commonsense generation by large language models via in-context learning T Zhang, B Peng, D Bollegala arXiv preprint arXiv:2404.16807, 2024 | 16 | 2024 |
| Centralised rehearsal of decentralised cooperation: Multi-agent reinforcement learning for the scalable coordination of residential energy flexibility F Charbonnier, B Peng, J Vienne, E Stai, T Morstyn, M McCulloch Applied Energy 377, 124406, 2025 | 15 | 2025 |
Matthew E. TaylorProfessor, University of AlbertaVerified email at ualberta.ca
