Grounding English Commands to Reward Functions. - James MacGlashan; Monica Babes-Vroman; Marie desJardins; Michael L. Littman; Smaranda Muresan; Shawn Squire; Stefanie Tellex; Dilip Arumugam; Lei Yang 003 (Robotics: Science and Systems, 2015)

A Tale of Two DRAGGNs: A Hybrid Approach for Interpreting Action-Oriented and Goal-Oriented Instructions. - Siddharth Karamcheti; Edward C. Williams; Dilip Arumugam; Mina Rhee; Nakul Gopalan; Lawson L. S. Wong; Stefanie Tellex (RoboNLP@ACL, 2017)

Accurately and Efficiently Interpreting Human-Robot Instructions of Varying Granularities. - Dilip Arumugam; Siddharth Karamcheti; Nakul Gopalan; Lawson L. S. Wong; Stefanie Tellex (Robotics: Science and Systems, 2017)

Latent Attention Networks. - Christopher Grimm; Dilip Arumugam; Siddharth Karamcheti; David Abel; Lawson L. S. Wong; Michael L. Littman (CoRR, 2017)

State Abstractions for Lifelong Reinforcement Learning. - David Abel; Dilip Arumugam; Lucas Lehnert; Michael L. Littman (ICML, 2018)

Sequence-to-Sequence Language Grounding of Non-Markovian Task Specifications. - Nakul Gopalan; Dilip Arumugam; Lawson L. S. Wong; Stefanie Tellex (Robotics: Science and Systems, 2018)

Mitigating Planner Overfitting in Model-Based Reinforcement Learning. - Dilip Arumugam; David Abel; Kavosh Asadi; Nakul Gopalan; Christopher Grimm; Jun Ki Lee; Lucas Lehnert; Michael L. Littman (CoRR, 2018)

State Abstraction as Compression in Apprenticeship Learning. - David Abel; Dilip Arumugam; Kavosh Asadi; Yuu Jinnai; Michael L. Littman; Lawson L. S. Wong (AAAI, 2019)

Grounding natural language instructions to semantic goal representations for abstraction and generalization. - Dilip Arumugam; Siddharth Karamcheti; Nakul Gopalan; Edward C. Williams; Mina Rhee; Lawson L. S. Wong; Stefanie Tellex (Auton. Robots, 2019)

Deep Reinforcement Learning from Policy-Dependent Human Feedback. - Dilip Arumugam; Jun Ki Lee; Sophie Saskin; Michael L. Littman (CoRR, 2019)

Value Preserving State-Action Abstractions. - David Abel; Nate Umbanhowar; Khimya Khetarpal; Dilip Arumugam; Doina Precup; Michael L. Littman (AISTATS, 2020)

Flexible and Efficient Long-Range Planning Through Curious Exploration. - Aidan Curtis; Minjian Xin; Dilip Arumugam; Kevin T. Feigelis; Daniel Yamins (ICML, 2020)

Reparameterized Variational Divergence Minimization for Stable Imitation. - Dilip Arumugam; Debadeepta Dey; Alekh Agarwal; Asli Celikyilmaz; Elnaz Nouri; Bill Dolan (CoRR, 2020)

Randomized Value Functions via Posterior State-Abstraction Sampling. - Dilip Arumugam; Benjamin Van Roy (CoRR, 2020)

Deciding What to Learn: A Rate-Distortion Approach. - Dilip Arumugam; Benjamin Van Roy (ICML, 2021)

The Value of Information When Deciding What to Learn. - Dilip Arumugam; Benjamin Van Roy (NeurIPS, 2021)

An Information-Theoretic Perspective on Credit Assignment in Reinforcement Learning. - Dilip Arumugam; Peter Henderson 002; Pierre-Luc Bacon (CoRR, 2021)

Bad-Policy Density: A Measure of Reinforcement Learning Hardness. - David Abel; Cameron Allen; Dilip Arumugam; D. Ellis Hershkowitz; Michael L. Littman; Lawson L. S. Wong (CoRR, 2021)

Planning to the Information Horizon of BAMDPs via Epistemic State Abstraction. - Dilip Arumugam; Satinder Singh 001 (NeurIPS, 2022)

Deciding What to Model: Value-Equivalent Sampling for Reinforcement Learning. - Dilip Arumugam; Benjamin Van Roy (NeurIPS, 2022)

Between Rate-Distortion Theory & Value Equivalence in Model-Based Reinforcement Learning. - Dilip Arumugam; Benjamin Van Roy (CoRR, 2022)

On Rate-Distortion Theory in Capacity-Limited Cognition & Reinforcement Learning. - Dilip Arumugam; Mark K. Ho; Noah D. Goodman; Benjamin Van Roy (CoRR, 2022)

Inclusive Artificial Intelligence. - Dilip Arumugam; Shi Dong 003; Benjamin Van Roy (CoRR, 2022)

Cultural reinforcement learning: a framework for modeling cumulative culture on a limited channel. - Ben Prystawski; Dilip Arumugam; Noah D. Goodman (CogSci, 2023)

Bayesian Reinforcement Learning with Limited Cognitive Load. - Dilip Arumugam; Mark K. Ho; Noah D. Goodman; Benjamin Van Roy (CoRR, 2023)

Shattering the Agent-Environment Interface for Fine-Tuning Inclusive Language Models. - Wanqiao Xu; Shi Dong 003; Dilip Arumugam; Benjamin Van Roy (CoRR, 2023)

Hindsight-DICE: Stable Credit Assignment for Deep Reinforcement Learning. - Akash Velu; Skanda Vaidyanath; Dilip Arumugam (CoRR, 2023)

Social Contract AI: Aligning AI Assistants with Implicit Group Norms. - Jan-Philipp Fränken; Sam Kwok; Peixuan Ye; Kanishk Gandhi; Dilip Arumugam; Jared Moore; Alex Tamkin; Tobias Gerstenberg; Noah D. Goodman (CoRR, 2023)

Exploration Unbound. - Dilip Arumugam; Wanqiao Xu; Benjamin Van Roy (CoRR, 2024)

Satisficing Exploration for Deep Reinforcement Learning. - Dilip Arumugam; Saurabh Kumar 004; Ramki Gummadi; Benjamin Van Roy (CoRR, 2024)

Hindsight Merging: Diverse Data Generation with Language Models. - Veniamin Veselovsky; Benedikt Stroebl; Gianluca M. Bencomo; Dilip Arumugam; Lisa Schut; Arvind Narayanan; Thomas L. Griffiths 001 (UAI, 2025)

Toward Efficient Exploration by Large Language Model Agents. - Dilip Arumugam; Thomas L. Griffiths 001 (CoRR, 2025)

Using Reinforcement Learning to Train Large Language Models to Explain Human Decisions. - Jian-Qiao Zhu; Hanbo Xie; Dilip Arumugam; Robert C. Wilson; Thomas L. Griffiths 001 (CoRR, 2025)

Are Large Language Models Reliable AI Scientists? Assessing Reverse-Engineering of Black-Box Systems. - Jiayi Geng; Howard Chen 003; Dilip Arumugam; Thomas L. Griffiths 001 (CoRR, 2025)