2023
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). Association for Computational Linguistics. doi:10.18653/v1/2023.ijcnlp-short.2DOI: 10.18653/v1/2023.ijcnlp-short.2
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_30DOI: 10.1007/978-3-031-47994-6_30
Learning to Predict Concept Ordering for Common Sense Generation. (Conference Paper)
Zhang, T., Bollegala, D., & Peng, B. (2023). Learning to Predict Concept Ordering for Common Sense Generation.. In J. C. Park, Y. Arase, B. Hu, W. Lu, D. Wijaya, A. Purwarianti, & A. A. Krisnadhi (Eds.), IJCNLP (2) (pp. 10-19). Association for Computational Linguistics. Retrieved from https://aclanthology.org/volumes/2023.ijcnlp-short/
2022
Huang, X., Peng, B., & Zhao, X. (2022). Dependable learning-enabled multiagent systems. AI COMMUNICATIONS, 35(4), 407-420. doi:10.3233/AIC-220128DOI: 10.3233/AIC-220128
2021
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/
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/
2020
Curriculum Learning for Reinforcement Learning Domains: A Framework and Survey (Journal article)
Peng, B. (2020). Curriculum Learning for Reinforcement Learning Domains: A Framework and Survey. Journal of Machine Learning Research.
2018
Curriculum Design for Machine Learners in Sequential Decision Tasks (Journal article)
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.2018.2829980DOI: 10.1109/tetci.2018.2829980
