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Reinforcement Learning in Factored Action Spaces using Tensor Decompositions
Anuj Mahajan · Mikayel Samvelyan · Lei Mao · Viktor Makoviichuk · Animesh Garg · Jean Kossaifi · Shimon Whiteson · Yuke Zhu · Anima Anandkumar

Tue Dec 14 12:15 PM -- 12:20 PM (PST) @
in Second Workshop on Quantum Tensor Networks in Machine Learning »

We present an extended abstract for the previously published work Tesseract, Mahajan et al. 2021, which proposes a novel solution for Reinforcement Learning (RL) in large, factored action spaces using tensor decompositions. The goal of this abstract is twofold: (1) To garner greater interest amongst the tensor research community for creating methods and analysis for approximate RL, (2) To elucidate the generalised setting of factored action spaces where tensor decompositions can be used. We use cooperative multi-agent reinforcement learning scenario as the exemplary setting where the action space is naturally factored across agents and learning becomes intractable without resorting to approximation on the underlying hypothesis space for candidate solutions.

Author Information

Anuj Mahajan (University of Oxford)

Anuj is doing a PhD in machine learning at the University of Oxford. His research focuses on using deep learning, probabilistic inference and optimisation methods for single and multi-agent reinforcement learning. Anuj has done his undergrad in Computer Science from the Indian Institute of Technology, Delhi. His PhD is funded by the Google DeepMind Scholarship and Drapers Scholarship.

Mikayel Samvelyan (University College London)
Lei Mao (NVIDIA)
Viktor Makoviichuk (NVIDIA)
Animesh Garg (University of Toronto, Nvidia, Vector Institute)

I am a CIFAR AI Chair Assistant Professor of Computer Science at the University of Toronto, a Faculty Member at the Vector Institute, and Sr. Researcher at Nvidia. My current research focuses on machine learning for perception and control in robotics.

Jean Kossaifi (NVIDIA Research)
Shimon Whiteson (University of Oxford)
Yuke Zhu (University of Texas - Austin)
Anima Anandkumar (NVIDIA / Caltech)

Anima Anandkumar is a Bren professor at Caltech CMS department and a director of machine learning research at NVIDIA. Her research spans both theoretical and practical aspects of large-scale machine learning. In particular, she has spearheaded research in tensor-algebraic methods, non-convex optimization, probabilistic models and deep learning. Anima is the recipient of several awards and honors such as the Bren named chair professorship at Caltech, Alfred. P. Sloan Fellowship, Young investigator awards from the Air Force and Army research offices, Faculty fellowships from Microsoft, Google and Adobe, and several best paper awards. Anima received her B.Tech in Electrical Engineering from IIT Madras in 2004 and her PhD from Cornell University in 2009. She was a postdoctoral researcher at MIT from 2009 to 2010, a visiting researcher at Microsoft Research New England in 2012 and 2014, an assistant professor at U.C. Irvine between 2010 and 2016, an associate professor at U.C. Irvine between 2016 and 2017 and a principal scientist at Amazon Web Services between 2016 and 2018.

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