Improving Efficiency and Robustness of Model-based Reinforcement Learning

Model-based reinforcement learning allows AI systems to learn and use predictive models of their environments to plan ahead, achieving tasks more efficiently. The proposed project aims to (i) develop methods for identifying when an uncertain and/or flawed model can be relied on to make plans, and when it cannot, and (ii) implement a method which allows an AI system to explore its environment exactly when exploration will be most useful for improving its model-based predictions and plans. Such methods for using models robustly, efficiently, and adaptively are promising for real-world applications of reinforcement learning which require systems to achieve tasks with limited data.

Faculty Supervisor:

Yaoliang Yu


Elliot Nelson


Borealis AI


Computer science



University of Waterloo



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