Matthew Taylor
Appointment
Canada CIFAR AI Chair
Pan-Canadian AI Strategy
About
Appointed Canada CIFAR AI Chair – 2020
Matthew Taylor is a Fellow and Canada CIFAR AI Chair at Amii and a Professor of Computing Science at the University of Alberta. He is the Director of the Intelligent Robot Learning (IRL) Lab and a Principal Investigator at the Reinforcement Learning & Artificial Intelligence (RLAI) Lab, at the University of Alberta.
Taylor’s research focuses on developing intelligent agents, physical or virtual entities that interact with their environments. His main goals are to enable individual agents, and teams of agents, to learn tasks in real-world environments that are not fully known when the agents are designed. Current approaches that his teams are investigating include improving reinforcement learning through demonstrations, teaching reinforcement learning systems through action advice, and training agents with discrete human feedback.
Awards
- Early Career Spotlight talk, IJCAI-18, 2018
- Awarded AAAI Senior Member Status, 2018
- WSU EECS Early Career Award, 2015
Relevant Publications
- Retzlaff, C. O., Das, S., Wayllace, C., Mousavi, P., Afshari, M., Yang, T., Saranti, A., Angerschmid, A., Taylor, M. E., & Holzinger, A. (2024). Human-in-the-Loop Reinforcement Learning: A Survey and Position on Requirements, Challenges, and Opportunities. Journal of Artificial Intelligence Research, 79, 359-415.
- N. Gupta, G. Srinivasaraghavan, S. Mohalik, N. Kumar, & M. E. Taylor. (2023). hammer: Multi-level coordination of reinforcement learning agents via learned messaging. Neural Computing and Applications, 1-16.
- Behboudian, P., Satsangi, Y., Taylor, M. E., Harutyunyan, A., & Bowling, M. (2022). Policy invariant explicit shaping: an efficient alternative to reward shaping. Neural Computing and Applications, 1-14.
- Yang, Y., Luo, J., Wen, Y., Slumbers, O., Graves, D., Bou Ammar, H., Wang, J., & Taylor, M. E. (2021). Diverse Auto-Curriculum is Critical for Successful Real-World Multiagent Learning Systems. In Proceedings of the Autonomous Agents and Multiagent Systems (AAMAS).
Da Silva, F. L., Hernandez-Leal, P., Kartal, B., & Taylor, M. E. (2020). Uncertainty-Aware Action Advising for Deep Reinforcement Learning Agents. In AAAI (pp. 5792-5799).