4 citations · 12 across the 10 of their papers we have counts for
5 papers · 1 filter
AMAGO-2: Breaking the Multi-Task Barrier in Meta-Reinforcement Learning with Transformers
Jake Grigsby, Justin Sasek, Samyak Parajuli +3
Language models trained on diverse datasets unlock generalization by in-context learning. Reinforcement Learning (RL) policies can achieve a similar effect by meta-learning within…
Explore and Control with Adversarial Surprise
Arnaud Fickinger, Natasha Jaques, Samyak Parajuli +5
Unsupervised reinforcement learning (RL) studies how to leverage environment statistics to learn useful behaviors without the cost of reward engineering. However, a central challen…
Dynamically Throttleable Neural Networks (TNN)
Hengyue Liu, Samyak Parajuli, Jesse Hostetler +2
Conditional computation for Deep Neural Networks (DNNs) reduce overall computational load and improve model accuracy by running a subset of the network. In this work, we present a…
Inter-Level Cooperation in Hierarchical Reinforcement Learning
Abdul Rahman Kreidieh, Glen Berseth, Brandon Trabucco +3
Hierarchies of temporally decoupled policies present a promising approach for enabling structured exploration in complex long-term planning problems. To fully achieve this approach…
Generalized Ternary Connect: End-to-End Learning and Compression of Multiplication-Free Deep Neural Networks
Samyak Parajuli, Aswin Raghavan, Sek Chai
The use of deep neural networks in edge computing devices hinges on the balance between accuracy and complexity of computations. Ternary Connect (TC) \cite{lin2015neural} addresses…