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An Information-Theoretic Perspective on Credit Assignment in Reinforcement Learning


Mar 10, 2021
Dilip Arumugam, Peter Henderson, Pierre-Luc Bacon

* Workshop on Biological and Artificial Reinforcement Learning (NeurIPS 2020) 

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Deciding What to Learn: A Rate-Distortion Approach


Jan 15, 2021
Dilip Arumugam, Benjamin Van Roy


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Randomized Value Functions via Posterior State-Abstraction Sampling


Oct 05, 2020
Dilip Arumugam, Benjamin Van Roy


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Reparameterized Variational Divergence Minimization for Stable Imitation


Jun 18, 2020
Dilip Arumugam, Debadeepta Dey, Alekh Agarwal, Asli Celikyilmaz, Elnaz Nouri, Bill Dolan


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Flexible and Efficient Long-Range Planning Through Curious Exploration


Apr 22, 2020
Aidan Curtis, Minjian Xin, Dilip Arumugam, Kevin Feigelis, Daniel Yamins


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Deep Reinforcement Learning from Policy-Dependent Human Feedback


Feb 12, 2019
Dilip Arumugam, Jun Ki Lee, Sophie Saskin, Michael L. Littman


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Mitigating Planner Overfitting in Model-Based Reinforcement Learning


Dec 03, 2018
Dilip Arumugam, David Abel, Kavosh Asadi, Nakul Gopalan, Christopher Grimm, Jun Ki Lee, Lucas Lehnert, Michael L. Littman


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Accurately and Efficiently Interpreting Human-Robot Instructions of Varying Granularities


Jun 19, 2018
Dilip Arumugam, Siddharth Karamcheti, Nakul Gopalan, Lawson L. S. Wong, Stefanie Tellex

* Updated with final version - Published as Conference Paper in Robotics: Science and Systems 2017 

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Modeling Latent Attention Within Neural Networks


Dec 30, 2017
Christopher Grimm, Dilip Arumugam, Siddharth Karamcheti, David Abel, Lawson L. S. Wong, Michael L. Littman


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A Tale of Two DRAGGNs: A Hybrid Approach for Interpreting Action-Oriented and Goal-Oriented Instructions


Jul 26, 2017
Siddharth Karamcheti, Edward C. Williams, Dilip Arumugam, Mina Rhee, Nakul Gopalan, Lawson L. S. Wong, Stefanie Tellex

* Accepted at the 1st Workshop on Language Grounding for Robotics at ACL 2017 

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