Research outputs
2026
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 ARTIFICIAL INTELLIGENCE XL, AI 2023 Vol. 14381 (pp. 321-334). doi:10.1007/978-3-031-47994-6_30
Heuristic Transformer: Belief Augmented In-Context Reinforcement Learning
Dippel, O., Peng, B., & Lisitsa, A. (2026). Heuristic Transformer: Belief Augmented In-Context Reinforcement Learning. In Proceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems Aamas Vol. 5 (pp. 3122-3124). doi:10.65109/IWMS8291
2025
Contextual Transformers for Goal-Oriented Reinforcement Learning
Dippel, O., Lisitsa, A., & Peng, B. (2025). Contextual Transformers for Goal-Oriented Reinforcement Learning. In ARTIFICIAL INTELLIGENCE XLI, SGAI 2024, PT I Vol. 15446 (pp. 207-220). 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/
Accelerating Laboratory Automation Through Robot Skill Learning For Sample Scraping
A Comparison Between Kalman-MLE and KalmanNet for State Estimation with Unknown Noise Parameters
Hanlon, B., Garcia-Fernandez, A. F., & Peng, B. (2024). A Comparison Between Kalman-MLE and KalmanNet for State Estimation with Unknown Noise Parameters. In IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems. 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 IEEE International Conference on Automation Science and Engineering (pp. 2103-2110). doi:10.1109/CASE59546.2024.10711291
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
2023
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 Long Papers Ijcnlp Aacl 2023 Vol. 2 (pp. 10-19). doi:10.18653/v1/2023.ijcnlp-short.2
2022
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 (pp. 14 pages). 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 (pp. 13 pages). 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