Research outputs
2025
Centralised rehearsal of decentralised cooperation: Multi-agent reinforcement learning for the scalable coordination of residential energy flexibility
Charbonnier, F., Peng, B., Vienne, J., Stai, E., Morstyn, T., & McCulloch, M. (2025). Centralised rehearsal of decentralised cooperation: Multi-agent reinforcement learning for the scalable coordination of residential energy flexibility. Applied Energy, 377, 124406. doi:10.1016/j.apenergy.2024.124406
Contextual Transformers for Goal-Oriented Reinforcement Learning
Dippel, O., Lisitsa, A., & Peng, B. (2025). Contextual Transformers for Goal-Oriented Reinforcement Learning. In Unknown Conference (pp. 207-220). Springer Nature Switzerland. doi:10.1007/978-3-031-77915-2_15
2024
Improving Diversity of Commonsense Generation by Large Language Models via In-Context Learning.
Zhang, T., Peng, B., & Bollegala, D. (2024). Improving Diversity of Commonsense Generation by Large Language Models via In-Context Learning.. In Y. Al-Onaizan, M. Bansal, & Y. -N. Chen (Eds.), EMNLP (Findings) (pp. 9226-9242). Association for Computational Linguistics. Retrieved from https://aclanthology.org/volumes/2024.findings-emnlp/
A Comparison Between Kalman-MLE and KalmanNet for State Estimation with Unknown Noise Parameters
Hanlon, B., García-Fernández, Á. F., & Peng, B. (2024). A Comparison Between Kalman-MLE and KalmanNet for State Estimation with Unknown Noise Parameters. In 2024 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI) (pp. 1-8). IEEE. doi:10.1109/mfi62651.2024.10705758
Accelerating Laboratory Automation Through Robot Skill Learning For Sample Scraping*
Pizzuto, G., Wang, H., Fakhruldeen, H., Peng, B., Luck, K. S., & Cooper, A. I. (2024). Accelerating Laboratory Automation Through Robot Skill Learning For Sample Scraping*. In 2024 IEEE 20th International Conference on Automation Science and Engineering (CASE) (pp. 2103-2110). IEEE. doi:10.1109/case59546.2024.10711291
2023
Deep Reinforcement Learning for Continuous Control of Material Thickness
Dippel, O., Lisitsa, A., & Peng, B. (2023). Deep Reinforcement Learning for Continuous Control of Material Thickness. In Unknown Conference (pp. 321-334). Springer Nature Switzerland. doi:10.1007/978-3-031-47994-6_30
Learning to Predict Concept Ordering for Common Sense Generation
Zhang, T., Bollegala, D., & Peng, B. (2023). Learning to Predict Concept Ordering for Common Sense Generation. In Proceedings of the 13th International Joint Conference on Natural Language Processing and the 3rd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics (Volume 2: Short Papers) (pp. 10-19). Association for Computational Linguistics. doi:10.18653/v1/2023.ijcnlp-short.2
2022
Accelerating Laboratory Automation Through Robot Skill Learning For Sample Scraping
Dependable learning-enabled multiagent systems
Huang, X., Peng, B., & Zhao, X. (2022). Dependable learning-enabled multiagent systems. AI COMMUNICATIONS, 35(4), 407-420. doi:10.3233/AIC-220128
2021
FACMAC: Factored Multi-Agent Centralised Policy Gradients
Peng, B., Rashid, T., de Witt, C. A. S., Kamienny, P. -A., Torr, P. H. S., & Bohmer, W. (2021). FACMAC: Factored Multi-Agent Centralised Policy Gradients. In ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 34 (NEURIPS 2021) Vol. 34. Retrieved from https://www.webofscience.com/
Regularized Softmax Deep Multi-Agent <i>Q-</i>Learning
Pan, L., Rashid, T., Peng, B., Huang, L., & Whiteson, S. (2021). Regularized Softmax Deep Multi-Agent <i>Q-</i>Learning. In ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 34 (NEURIPS 2021) Vol. 34. Retrieved from https://www.webofscience.com/
2018
Curriculum Design for Machine Learners in Sequential Decision Tasks
Peng, B., MacGlashan, J., Loftin, R., Littman, M. L., Roberts, D. L., & Taylor, M. E. (2018). Curriculum Design for Machine Learners in Sequential Decision Tasks. IEEE Transactions on Emerging Topics in Computational Intelligence, 2(4), 268-277. doi:10.1109/tetci.2018.2829980