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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.
Related Events (a corresponding poster, oral, or spotlight)
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2021 : Reinforcement Learning in Factored Action Spaces using Tensor Decompositions »
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Rahul Mehta · Andrew Lampinen · Binghong Chen · Sergio Pascual-Diaz · Jordi Grau-Moya · Aldo Faisal · Jonathan Tompson · Yiren Lu · Khimya Khetarpal · Martin Klissarov · Pierre-Luc Bacon · Doina Precup · Thanard Kurutach · Aviv Tamar · Pieter Abbeel · Jinke He · Maximilian Igl · Shimon Whiteson · Wendelin Boehmer · Raphaël Marinier · Olivier Pietquin · Karol Hausman · Sergey Levine · Chelsea Finn · Tianhe Yu · Lisa Lee · Benjamin Eysenbach · Emilio Parisotto · Eric Xing · Ruslan Salakhutdinov · Hongyu Ren · Anima Anandkumar · Deepak Pathak · Christopher Lu · Trevor Darrell · Alexei Efros · Phillip Isola · Feng Liu · Bo Han · Gang Niu · Masashi Sugiyama · Saurabh Kumar · Janith Petangoda · Johan Ferret · James McClelland · Kara Liu · Animesh Garg · Robert Lange -
2019 : Poster Session »
Matthia Sabatelli · Adam Stooke · Amir Abdi · Paulo Rauber · Leonard Adolphs · Ian Osband · Hardik Meisheri · Karol Kurach · Johannes Ackermann · Matt Benatan · GUO ZHANG · Chen Tessler · Dinghan Shen · Mikayel Samvelyan · Riashat Islam · Murtaza Dalal · Luke Harries · Andrey Kurenkov · Konrad Żołna · Sudeep Dasari · Kristian Hartikainen · Ofir Nachum · Kimin Lee · Markus Holzleitner · Vu Nguyen · Francis Song · Christopher Grimm · Felipe Leno da Silva · Yuping Luo · Yifan Wu · Alex Lee · Thomas Paine · Wei-Yang Qu · Daniel Graves · Yannis Flet-Berliac · Yunhao Tang · Suraj Nair · Matthew Hausknecht · Akhil Bagaria · Simon Schmitt · Bowen Baker · Paavo Parmas · Benjamin Eysenbach · Lisa Lee · Siyu Lin · Daniel Seita · Abhishek Gupta · Riley Simmons-Edler · Yijie Guo · Kevin Corder · Vikash Kumar · Scott Fujimoto · Adam Lerer · Ignasi Clavera Gilaberte · Nicholas Rhinehart · Ashvin Nair · Ge Yang · Lingxiao Wang · Sungryull Sohn · J. Fernando Hernandez-Garcia · Xian Yeow Lee · Rupesh Srivastava · Khimya Khetarpal · Chenjun Xiao · Luckeciano Carvalho Melo · Rishabh Agarwal · Tianhe Yu · Glen Berseth · Devendra Singh Chaplot · Jie Tang · Anirudh Srinivasan · Tharun Kumar Reddy Medini · Aaron Havens · Misha Laskin · Asier Mujika · Rohan Saphal · Joseph Marino · Alex Ray · Joshua Achiam · Ajay Mandlekar · Zhuang Liu · Danijar Hafner · Zhiwen Tang · Ted Xiao · Michael Walton · Jeff Druce · Ferran Alet · Zhang-Wei Hong · Stephanie Chan · Anusha Nagabandi · Hao Liu · Hao Sun · Ge Liu · Dinesh Jayaraman · John Co-Reyes · Sophia Sanborn -
2019 : Bayes-Adaptive Deep Reinforcement Learning via Meta-Learning - Invited Talk »
Shimon Whiteson -
2019 : Poster and Coffee Break 1 »
Aaron Sidford · Aditya Mahajan · Alejandro Ribeiro · Alex Lewandowski · Ali H Sayed · Ambuj Tewari · Angelika Steger · Anima Anandkumar · Asier Mujika · Hilbert J Kappen · Bolei Zhou · Byron Boots · Chelsea Finn · Chen-Yu Wei · Chi Jin · Ching-An Cheng · Christina Yu · Clement Gehring · Craig Boutilier · Dahua Lin · Daniel McNamee · Daniel Russo · David Brandfonbrener · Denny Zhou · Devesh Jha · Diego Romeres · Doina Precup · Dominik Thalmeier · Eduard Gorbunov · Elad Hazan · Elena Smirnova · Elvis Dohmatob · Emma Brunskill · Enrique Munoz de Cote · Ethan Waldie · Florian Meier · Florian Schaefer · Ge Liu · Gergely Neu · Haim Kaplan · Hao Sun · Hengshuai Yao · Jalaj Bhandari · James A Preiss · Jayakumar Subramanian · Jiajin Li · Jieping Ye · Jimmy Smith · Joan Bas Serrano · Joan Bruna · John Langford · Jonathan Lee · Jose A. Arjona-Medina · Kaiqing Zhang · Karan Singh · Yuping Luo · Zafarali Ahmed · Zaiwei Chen · Zhaoran Wang · Zhizhong Li · Zhuoran Yang · Ziping Xu · Ziyang Tang · Yi Mao · David Brandfonbrener · Shirli Di-Castro · Riashat Islam · Zuyue Fu · Abhishek Naik · Saurabh Kumar · Benjamin Petit · Angeliki Kamoutsi · Simone Totaro · Arvind Raghunathan · Rui Wu · Donghwan Lee · Dongsheng Ding · Alec Koppel · Hao Sun · Christian Tjandraatmadja · Mahdi Karami · Jincheng Mei · Chenjun Xiao · Junfeng Wen · Zichen Zhang · Ross Goroshin · Mohammad Pezeshki · Jiaqi Zhai · Philip Amortila · Shuo Huang · Mariya Vasileva · El houcine Bergou · Adel Ahmadyan · Haoran Sun · Sheng Zhang · Lukas Gruber · Yuanhao Wang · Tetiana Parshakova -
2019 : Opening Remarks »
Atilim Gunes Baydin · Juan Carrasquilla · Shirley Ho · Karthik Kashinath · Michela Paganini · Savannah Thais · Anima Anandkumar · Kyle Cranmer · Roger Melko · Mr. Prabhat · Frank Wood -
2019 Workshop: Machine Learning and the Physical Sciences »
Atilim Gunes Baydin · Juan Carrasquilla · Shirley Ho · Karthik Kashinath · Michela Paganini · Savannah Thais · Anima Anandkumar · Kyle Cranmer · Roger Melko · Mr. Prabhat · Frank Wood -
2019 Poster: Regression Planning Networks »
Danfei Xu · Roberto Martín-Martín · De-An Huang · Yuke Zhu · Silvio Savarese · Li Fei-Fei -
2019 Poster: Competitive Gradient Descent »
Florian Schaefer · Anima Anandkumar -
2019 Poster: MAVEN: Multi-Agent Variational Exploration »
Anuj Mahajan · Tabish Rashid · Mikayel Samvelyan · Shimon Whiteson -
2019 Poster: Loaded DiCE: Trading off Bias and Variance in Any-Order Score Function Gradient Estimators for Reinforcement Learning »
Gregory Farquhar · Shimon Whiteson · Jakob Foerster -
2019 Poster: Multi-Agent Common Knowledge Reinforcement Learning »
Christian Schroeder de Witt · Jakob Foerster · Gregory Farquhar · Philip Torr · Wendelin Boehmer · Shimon Whiteson -
2019 Poster: DAC: The Double Actor-Critic Architecture for Learning Options »
Shangtong Zhang · Shimon Whiteson -
2019 Poster: Fast Efficient Hyperparameter Tuning for Policy Gradient Methods »
Supratik Paul · Vitaly Kurin · Shimon Whiteson -
2019 Poster: VIREL: A Variational Inference Framework for Reinforcement Learning »
Mattie Fellows · Anuj Mahajan · Tim G. J. Rudner · Shimon Whiteson -
2019 Spotlight: VIREL: A Variational Inference Framework for Reinforcement Learning »
Mattie Fellows · Anuj Mahajan · Tim G. J. Rudner · Shimon Whiteson -
2019 Poster: Generalized Off-Policy Actor-Critic »
Shangtong Zhang · Wendelin Boehmer · Shimon Whiteson -
2018 : Poster Session »
Sujay Sanghavi · Vatsal Shah · Yanyao Shen · Tianchen Zhao · Yuandong Tian · Tomer Galanti · Mufan Li · Gilad Cohen · Daniel Rothchild · Aristide Baratin · Devansh Arpit · Vagelis Papalexakis · Michael Perlmutter · Ashok Vardhan Makkuva · Pim de Haan · Yingyan Lin · Wanmo Kang · Cheolhyoung Lee · Hao Shen · Sho Yaida · Dan Roberts · Nadav Cohen · Philippe Casgrain · Dejiao Zhang · Tengyu Ma · Avinash Ravichandran · Julian Emilio Salazar · Bo Li · Davis Liang · Christopher Wong · Glen Bigan Mbeng · Animesh Garg -
2018 Workshop: Integration of Deep Learning Theories »
Richard Baraniuk · Anima Anandkumar · Stephane Mallat · Ankit Patel · nhật Hồ -
2017 Poster: Dynamic-Depth Context Tree Weighting »
Joao V Messias · Shimon Whiteson -
2016 : Anima Anandkumar »
Anima Anandkumar -
2016 Workshop: Learning with Tensors: Why Now and How? »
Anima Anandkumar · Rong Ge · Yan Liu · Maximilian Nickel · Qi (Rose) Yu -
2016 : Learning to Communicate with Deep Multi−Agent Reinforcement Learning »
Shimon Whiteson -
2016 Workshop: Nonconvex Optimization for Machine Learning: Theory and Practice »
Hossein Mobahi · Anima Anandkumar · Percy Liang · Stefanie Jegelka · Anna Choromanska -
2016 Poster: Learning to Communicate with Deep Multi-Agent Reinforcement Learning »
Jakob Foerster · Yannis Assael · Nando de Freitas · Shimon Whiteson -
2016 Poster: Online and Differentially-Private Tensor Decomposition »
Yining Wang · Anima Anandkumar -
2015 : Opening and Overview »
Anima Anandkumar -
2015 Workshop: Non-convex Optimization for Machine Learning: Theory and Practice »
Anima Anandkumar · Niranjan Uma Naresh · Kamalika Chaudhuri · Percy Liang · Sewoong Oh -
2015 Poster: Copeland Dueling Bandits »
Masrour Zoghi · Zohar Karnin · Shimon Whiteson · Maarten de Rijke -
2015 Poster: Fast and Guaranteed Tensor Decomposition via Sketching »
Yining Wang · Hsiao-Yu Tung · Alexander Smola · Anima Anandkumar -
2015 Spotlight: Fast and Guaranteed Tensor Decomposition via Sketching »
Yining Wang · Hsiao-Yu Tung · Alexander Smola · Anima Anandkumar -
2014 Poster: Multi-Step Stochastic ADMM in High Dimensions: Applications to Sparse Optimization and Matrix Decomposition »
Hanie Sedghi · Anima Anandkumar · Edmond A Jonckheere -
2014 Poster: Non-convex Robust PCA »
Praneeth Netrapalli · Niranjan Uma Naresh · Sujay Sanghavi · Animashree Anandkumar · Prateek Jain -
2014 Spotlight: Non-convex Robust PCA »
Praneeth Netrapalli · Niranjan Uma Naresh · Sujay Sanghavi · Animashree Anandkumar · Prateek Jain -
2013 Workshop: Topic Models: Computation, Application, and Evaluation »
David Mimno · Amr Ahmed · Jordan Boyd-Graber · Ankur Moitra · Hanna Wallach · Alexander Smola · David Blei · Anima Anandkumar -
2013 Poster: When are Overcomplete Topic Models Identifiable? Uniqueness of Tensor Tucker Decompositions with Structured Sparsity »
Anima Anandkumar · Daniel Hsu · Majid Janzamin · Sham M Kakade -
2012 Poster: Learning Mixtures of Tree Graphical Models »
Anima Anandkumar · Daniel Hsu · Furong Huang · Sham M Kakade -
2012 Poster: A Spectral Algorithm for Latent Dirichlet Allocation »
Anima Anandkumar · Dean P Foster · Daniel Hsu · Sham M Kakade · Yi-Kai Liu -
2012 Spotlight: A Spectral Algorithm for Latent Dirichlet Allocation »
Anima Anandkumar · Dean P Foster · Daniel Hsu · Sham M Kakade · Yi-Kai Liu -
2012 Poster: Latent Graphical Model Selection: Efficient Methods for Locally Tree-like Graphs »
Anima Anandkumar · Ragupathyraj Valluvan -
2011 Poster: High-Dimensional Graphical Model Selection: Tractable Graph Families and Necessary Conditions »
Animashree Anandkumar · Vincent Tan · Alan S Willsky -
2011 Oral: High-Dimensional Graphical Model Selection: Tractable Graph Families and Necessary Conditions »
Animashree Anandkumar · Vincent Tan · Alan S Willsky -
2011 Poster: Spectral Methods for Learning Multivariate Latent Tree Structure »
Anima Anandkumar · Kamalika Chaudhuri · Daniel Hsu · Sham M Kakade · Le Song · Tong Zhang