Ruo Yu (David) Tao
Computer Science M.Sc. @ UAlberta
Email: rtao3 "at" ualberta "dot" ca
I'm currently a Computing Science M.Sc. (Thesis) student at the University of Alberta, co-supervised by Adam White and Marlos C. Machado in the RLAI lab. My current research interests and current work are on agent-state learning for partially observable environments and representation learning for sequential decision making.
Previous to this, I was a First-class Honours and Dean's Honour List student studying Computer Science at McGill University. I was previously a research intern at both Mila and Microsoft Research. If you're interested in collaborating on something shoot me an email!
Novelty Search in Representational Space for Sample Efficient Exploration
Oral presentation, NeurIPS 2020
Towards Solving Text-based Games by Producing Adaptive Action Spaces
Oral presentation, WordPlay workshop NeurIPS 2018
TextWorld: A Learning Environment for Text-based Games
Computer Games Workshop IJCAI 2018
National University of Singapore (NUS)
Over the summer/during the pandemic lockdown I was working on a research project under Prof. Lee Wee Sun on neural-based memory for RL and SLAM as a research assistant.
Mila (Quebec AI Institute)
I've been working on a research project in exploration in a model-based reinforcement learning setting since my final year of my undergraduate degree at McGill. The project is mentored by Vincent François-Lavet and supervised by Joelle Pineau. Our paper on this was accepted to NeurIPS 2020 for an oral presentation. Check out our paper above!
Microsoft Research Montreal
During my time at Microsoft Research, I worked on reinforcement learning and natural language problems. I was advised by Marc-Alexandre Côté on projects including:
Adaptive text action spaces: see the above paper!
TextWorld: a reinforcement learning framework for agents to learn text-based games. Also see above!