Dilip Arumugam | DeepAI

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∙ 07/21/2023

Hindsight-DICE: Stable Credit Assignment for Deep Reinforcement Learning

Oftentimes, environments for sequential decision-making problems can be ...

0 Akash Velu, et al. ∙

∙ 05/19/2023

Shattering the Agent-Environment Interface for Fine-Tuning Inclusive Language Models

A centerpiece of the ever-popular reinforcement learning from human feed...

0 Wanqiao Xu, et al. ∙

∙ 05/05/2023

Bayesian Reinforcement Learning with Limited Cognitive Load

All biological and artificial agents must learn and make decisions given...

0 Dilip Arumugam, et al. ∙

∙ 12/24/2022

Inclusive Artificial Intelligence

Prevailing methods for assessing and comparing generative AIs incentiviz...

0 Dilip Arumugam, et al. ∙

∙ 10/30/2022

On Rate-Distortion Theory in Capacity-Limited Cognition Reinforcement Learning

Throughout the cognitive-science literature, there is widespread agreeme...

0 Dilip Arumugam, et al. ∙

∙ 10/30/2022

Planning to the Information Horizon of BAMDPs via Epistemic State Abstraction

The Bayes-Adaptive Markov Decision Process (BAMDP) formalism pursues the...

0 Dilip Arumugam, et al. ∙

∙ 06/04/2022

Deciding What to Model: Value-Equivalent Sampling for Reinforcement Learning

The quintessential model-based reinforcement-learning agent iteratively ...

0 Dilip Arumugam, et al. ∙

∙ 06/04/2022

Between Rate-Distortion Theory Value Equivalence in Model-Based Reinforcement Learning

The quintessential model-based reinforcement-learning agent iteratively ...

0 Dilip Arumugam, et al. ∙

∙ 10/26/2021

The Value of Information When Deciding What to Learn

All sequential decision-making agents explore so as to acquire knowledge...

0 Dilip Arumugam, et al. ∙

∙ 10/07/2021

Bad-Policy Density: A Measure of Reinforcement Learning Hardness

Reinforcement learning is hard in general. Yet, in many specific environ...

7 David Abel, et al. ∙

∙ 03/10/2021

An Information-Theoretic Perspective on Credit Assignment in Reinforcement Learning

How do we formalize the challenge of credit assignment in reinforcement ...

0 Dilip Arumugam, et al. ∙

∙ 01/15/2021

Deciding What to Learn: A Rate-Distortion Approach

Agents that learn to select optimal actions represent a prominent focus ...

0 Dilip Arumugam, et al. ∙

∙ 10/05/2020

Randomized Value Functions via Posterior State-Abstraction Sampling

State abstraction has been an essential tool for dramatically improving ...

0 Dilip Arumugam, et al. ∙

∙ 06/18/2020

Reparameterized Variational Divergence Minimization for Stable Imitation

While recent state-of-the-art results for adversarial imitation-learning...

0 Dilip Arumugam, et al. ∙

∙ 04/22/2020

Flexible and Efficient Long-Range Planning Through Curious Exploration

Identifying algorithms that flexibly and efficiently discover temporally...

0 Aidan Curtis, et al. ∙

∙ 02/12/2019

Deep Reinforcement Learning from Policy-Dependent Human Feedback

To widen their accessibility and increase their utility, intelligent age...

18 Dilip Arumugam, et al. ∙

∙ 12/03/2018

Mitigating Planner Overfitting in Model-Based Reinforcement Learning

An agent with an inaccurate model of its environment faces a difficult c...

0 Dilip Arumugam, et al. ∙

∙ 07/26/2017

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

Robots operating alongside humans in diverse, stochastic environments mu...

0 Siddharth Karamcheti, et al. ∙

∙ 06/02/2017

Latent Attention Networks

Deep neural networks are able to solve tasks across a variety of domains...

0 Christopher Grimm, et al. ∙

∙ 04/21/2017

Accurately and Efficiently Interpreting Human-Robot Instructions of Varying Granularities

Humans can ground natural language commands to tasks at both abstract an...

0 Dilip Arumugam, et al. ∙

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