31 citations · 72 across the 22 of their papers we have counts for
5 papers · 1 filter
Inclusive Artificial Intelligence
Dilip Arumugam, Shi Dong, Benjamin Van Roy
Prevailing methods for assessing and comparing generative AIs incentivize responses that serve a hypothetical representative individual. Evaluating models in these terms presumes h…
On Rate-Distortion Theory in Capacity-Limited Cognition & Reinforcement Learning
Dilip Arumugam, Mark K. Ho, Noah D. Goodman +1
Throughout the cognitive-science literature, there is widespread agreement that decision-making agents operating in the real world do so under limited information-processing capabi…
Planning to the Information Horizon of BAMDPs via Epistemic State Abstraction
Dilip Arumugam, Satinder Singh
The Bayes-Adaptive Markov Decision Process (BAMDP) formalism pursues the Bayes-optimal solution to the exploration-exploitation trade-off in reinforcement learning. As the computat…
Between Rate-Distortion Theory & Value Equivalence in Model-Based Reinforcement Learning
Dilip Arumugam, Benjamin Van Roy
The quintessential model-based reinforcement-learning agent iteratively refines its estimates or prior beliefs about the true underlying model of the environment. Recent empirical…
Deciding What to Model: Value-Equivalent Sampling for Reinforcement Learning
Dilip Arumugam, Benjamin Van Roy
The quintessential model-based reinforcement-learning agent iteratively refines its estimates or prior beliefs about the true underlying model of the environment. Recent empirical…