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

Tue Jun 11 11:35 AM -- 11:40 AM (PDT) @ Hall B

Hierarchical reinforcement learning (HRL) can provide a principled solution to the RL challenge of scalability for complex tasks. By incorporating a graphical model (GM) and the rich family of related methods, there is also hope to address issues such as transferability, generalisation and exploration. Here we propose a flexible GM-based HRL 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

Adrian Weller is Programme Director for AI at The Alan Turing Institute, the UK national institute for data science and AI, and is a Turing AI Fellow leading work on trustworthy Machine Learning (ML). He is a Principal Research Fellow in ML at the University of Cambridge, and at the Leverhulme Centre for the Future of Intelligence where he is Programme Director for Trust and Society. His interests span AI, its commercial applications and helping to ensure beneficial outcomes for society. 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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