31 citations · 39 across the 14 of their papers we have counts for
13 papers · 1 filter
Satisficing Exploration for Deep Reinforcement Learning
Dilip Arumugam, Saurabh Kumar, Ramki Gummadi +1
A default assumption in the design of reinforcement-learning algorithms is that a decision-making agent always explores to learn optimal behavior. In sufficiently complex environme…
Exploration Unbound
Dilip Arumugam, Wanqiao Xu, Benjamin Van Roy
A sequential decision-making agent balances between exploring to gain new knowledge about an environment and exploiting current knowledge to maximize immediate reward. For environm…
Hindsight-DICE: Stable Credit Assignment for Deep Reinforcement Learning
Akash Velu, Skanda Vaidyanath, Dilip Arumugam
Oftentimes, environments for sequential decision-making problems can be quite sparse in the provision of evaluative feedback to guide reinforcement-learning agents. In the extreme…
Bayesian Reinforcement Learning with Limited Cognitive Load
Dilip Arumugam, Mark K. Ho, Noah D. Goodman +1
All biological and artificial agents must learn and make decisions given limits on their ability to process information. As such, a general theory of adaptive behavior should be ab…
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…