Dilip Arumugam

Publication Activity (10 Years)

Years Active: 2015-2024
Publications (10 Years): 37

Top Topics

Reinforcement Learning

Reward Function

Natural Language Interfaces To Databases

Top Venues

Robotics: Science and Systems

Publications

Dilip Arumugam, Wanqiao Xu, Benjamin Van Roy
Exploration Unbound. CoRR (2024)
Dilip Arumugam, Saurabh Kumar, Ramki Gummadi, Benjamin Van Roy
Satisficing Exploration for Deep Reinforcement Learning. CoRR (2024)
Akash Velu, Skanda Vaidyanath, Dilip Arumugam
Hindsight-DICE: Stable Credit Assignment for Deep Reinforcement Learning. CoRR (2023)
Wanqiao Xu, Shi Dong, Dilip Arumugam, Benjamin Van Roy
Shattering the Agent-Environment Interface for Fine-Tuning Inclusive Language Models. CoRR (2023)
Ben Prystawski, Dilip Arumugam, Noah D. Goodman
Cultural reinforcement learning: a framework for modeling cumulative culture on a limited channel. CogSci (2023)
Jan-Philipp Fränken, Sam Kwok, Peixuan Ye, Kanishk Gandhi, Dilip Arumugam, Jared Moore, Alex Tamkin, Tobias Gerstenberg, Noah D. Goodman
Social Contract AI: Aligning AI Assistants with Implicit Group Norms. CoRR (2023)
Dilip Arumugam, Mark K. Ho, Noah D. Goodman, Benjamin Van Roy
Bayesian Reinforcement Learning with Limited Cognitive Load. CoRR (2023)
Dilip Arumugam, Mark K. Ho, Noah D. Goodman, Benjamin Van Roy
On Rate-Distortion Theory in Capacity-Limited Cognition & Reinforcement Learning. CoRR (2022)
Dilip Arumugam, Satinder Singh
Planning to the Information Horizon of BAMDPs via Epistemic State Abstraction. CoRR (2022)
Dilip Arumugam, Benjamin Van Roy
Between Rate-Distortion Theory & Value Equivalence in Model-Based Reinforcement Learning. CoRR (2022)
Dilip Arumugam, Benjamin Van Roy
Deciding What to Model: Value-Equivalent Sampling for Reinforcement Learning. NeurIPS (2022)
Dilip Arumugam, Satinder Singh
Planning to the Information Horizon of BAMDPs via Epistemic State Abstraction. NeurIPS (2022)
Dilip Arumugam, Benjamin Van Roy
Deciding What to Model: Value-Equivalent Sampling for Reinforcement Learning. CoRR (2022)
Dilip Arumugam, Shi Dong, Benjamin Van Roy
Inclusive Artificial Intelligence. CoRR (2022)
Dilip Arumugam, Benjamin Van Roy
Deciding What to Learn: A Rate-Distortion Approach. ICML (2021)
David Abel, Cameron Allen, Dilip Arumugam, D. Ellis Hershkowitz, Michael L. Littman, Lawson L. S. Wong
Bad-Policy Density: A Measure of Reinforcement Learning Hardness. CoRR (2021)
Dilip Arumugam, Peter Henderson, Pierre-Luc Bacon
An Information-Theoretic Perspective on Credit Assignment in Reinforcement Learning. CoRR (2021)
Dilip Arumugam, Benjamin Van Roy
The Value of Information When Deciding What to Learn. CoRR (2021)
Dilip Arumugam, Benjamin Van Roy
Deciding What to Learn: A Rate-Distortion Approach. CoRR (2021)
Dilip Arumugam, Benjamin Van Roy
The Value of Information When Deciding What to Learn. NeurIPS (2021)
David Abel, Nate Umbanhowar, Khimya Khetarpal, Dilip Arumugam, Doina Precup, Michael L. Littman
Value Preserving State-Action Abstractions. AISTATS (2020)
Aidan Curtis, Minjian Xin, Dilip Arumugam, Kevin T. Feigelis, Daniel Yamins
Flexible and Efficient Long-Range Planning Through Curious Exploration. ICML (2020)
Dilip Arumugam, Debadeepta Dey, Alekh Agarwal, Asli Celikyilmaz, Elnaz Nouri, Bill Dolan
Reparameterized Variational Divergence Minimization for Stable Imitation. CoRR (2020)
Dilip Arumugam, Benjamin Van Roy
Randomized Value Functions via Posterior State-Abstraction Sampling. CoRR (2020)
Aidan Curtis, Minjian Xin, Dilip Arumugam, Kevin T. Feigelis, Daniel Yamins
Flexible and Efficient Long-Range Planning Through Curious Exploration. CoRR (2020)
David Abel, Dilip Arumugam, Kavosh Asadi, Yuu Jinnai, Michael L. Littman, Lawson L. S. Wong
State Abstraction as Compression in Apprenticeship Learning. AAAI (2019)
Dilip Arumugam, Siddharth Karamcheti, Nakul Gopalan, Edward C. Williams, Mina Rhee, Lawson L. S. Wong, Stefanie Tellex
Grounding natural language instructions to semantic goal representations for abstraction and generalization. Auton. Robots 43 (2) (2019)
Dilip Arumugam, Jun Ki Lee, Sophie Saskin, Michael L. Littman
Deep Reinforcement Learning from Policy-Dependent Human Feedback. CoRR (2019)
Dilip Arumugam, David Abel, Kavosh Asadi, Nakul Gopalan, Christopher Grimm, Jun Ki Lee, Lucas Lehnert, Michael L. Littman
Mitigating Planner Overfitting in Model-Based Reinforcement Learning. CoRR (2018)
Nakul Gopalan, Dilip Arumugam, Lawson L. S. Wong, Stefanie Tellex
Sequence-to-Sequence Language Grounding of Non-Markovian Task Specifications. Robotics: Science and Systems (2018)
David Abel, Dilip Arumugam, Lucas Lehnert, Michael L. Littman
State Abstractions for Lifelong Reinforcement Learning. ICML (2018)
Christopher Grimm, Dilip Arumugam, Siddharth Karamcheti, David Abel, Lawson L. S. Wong, Michael L. Littman
Latent Attention Networks. CoRR (2017)
Dilip Arumugam, Siddharth Karamcheti, Nakul Gopalan, Lawson L. S. Wong, Stefanie Tellex
Accurately and Efficiently Interpreting Human-Robot Instructions of Varying Granularities. CoRR (2017)
Siddharth Karamcheti, Edward C. Williams, Dilip Arumugam, Mina Rhee, Nakul Gopalan, Lawson L. S. Wong, Stefanie Tellex
A Tale of Two DRAGGNs: A Hybrid Approach for Interpreting Action-Oriented and Goal-Oriented Instructions. RoboNLP@ACL (2017)
Dilip Arumugam, Siddharth Karamcheti, Nakul Gopalan, Lawson L. S. Wong, Stefanie Tellex
Accurately and Efficiently Interpreting Human-Robot Instructions of Varying Granularities. Robotics: Science and Systems (2017)
Siddharth Karamcheti, Edward C. Williams, Dilip Arumugam, Mina Rhee, Nakul Gopalan, Lawson L. S. Wong, Stefanie Tellex
A Tale of Two DRAGGNs: A Hybrid Approach for Interpreting Action-Oriented and Goal-Oriented Instructions. CoRR (2017)
James MacGlashan, Monica Babes-Vroman, Marie desJardins, Michael L. Littman, Smaranda Muresan, Shawn Squire, Stefanie Tellex, Dilip Arumugam, Lei Yang
Grounding English Commands to Reward Functions. Robotics: Science and Systems (2015)