Qizhen (Irene) Zhang

I am an Msc student in computer science at the University of Toronto , affiliated with the Vector Institute. I am fortunate to be advised by Jakob Foerster and Animesh Garg . Currently, I am working on multi-agent and generalization problems in reinforcement learning.

Previously, I completed my bachelors in computer science from McGill University. During my undergrad, I spent sometime as a research intern at Mila , where I was advised by Joelle Pineau, Audrey Durand, and Pierre-Luc Bacon.

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Highlighted Work

I am generally interested in research which lies at the intersection of deep learning and reinforcement learning.

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Centralized Model and Exploration Policy for Multi-Agent RL

Qizhen Zhang, Chris Lu, Animesh Garg, Jakob Foerster
International Conference on Autonomous Agents and Multiagent Systems (AAMAS), 2022. Full paper, Oral Presentation.
arxiv / talk /

We propose a model-based method for fully cooperative multi-agent settings (Dec-POMDPs). Our method learns a centralized model, and is up to 20x more sample efficient in three commuication tasks. We also show theoretical sample complexity bounds for model-based methods learning in tabular Dec-POMDPs.


I am/was a teaching assistant for the following courses.


CSC311: Introduction to Machine Learning

CSC413/2516: Neural Networks and Deep Learning

CSC384: Introduction to Artificial Intelligence

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