Poster
TibGM: A Transferable and Information-Based Graphical Model Approach for Reinforcement Learning
Tameem Adel · Adrian Weller

Tue Jun 11th 06:30 -- 09:00 PM @ Pacific Ballroom #35

One of the challenges to reinforcement learning (RL) is scalable transferability among complex tasks. Incorporating a graphical model (GM), along with the rich family of related methods, as a basis for RL frameworks provides potential to address issues such as transferability, generalisation and exploration. Here we propose a flexible GM-based RL framework which leverages efficient inference procedures to enhance generalisation and transfer power. In our proposed transferable and information-based graphical model framework ‘TibGM’, we show the equivalence between our mutual information-based objective in the GM, and an RL consolidated objective consisting of a standard reward maximisation target and a generalisation/transfer objective. In settings where there is a sparse or deceptive reward signal, our TibGM framework is flexible enough to incorporate exploration bonuses depicting intrinsic rewards. We empirically verify improved performance and exploration power.

Author Information

Tameem Adel (University of Cambridge)
Adrian Weller (University of Cambridge, Alan Turing Institute)

Adrian Weller is a Senior Research Fellow in the Machine Learning Group at the University of Cambridge, a Faculty Fellow at the Alan Turing Institute for data science and an Executive Fellow at the Leverhulme Centre for the Future of Intelligence (CFI). He is very interested in all aspects of artificial intelligence, its commercial applications and how it may be used to benefit society. At the CFI, he leads their project on Trust and Transparency. Previously, Adrian held senior roles in finance. He received a PhD in computer science from Columbia University, and an undergraduate degree in mathematics from Trinity College, Cambridge.

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