8 citations · 11 across the 11 of their papers we have counts for
4 papers · 1 filter
Improving and Accelerating Offline RL in Large Discrete Action Spaces with Structured Policy Initialization
Matthew Landers, Taylor W. Killian, Thomas Hartvigsen +1
Reinforcement learning in discrete combinatorial action spaces requires searching over exponentially many joint actions to simultaneously select multiple sub-actions that form cohe…
SAINT: Attention-Based Policies for Discrete Combinatorial Action Spaces
Matthew Landers, Taylor W. Killian, Thomas Hartvigsen +1
The combinatorial structure of many real-world action spaces leads to exponential growth in the number of possible actions, limiting the effectiveness of conventional reinforcement…
BraVE: Offline Reinforcement Learning for Discrete Combinatorial Action Spaces
Matthew Landers, Taylor W. Killian, Hugo Barnes +2
Offline reinforcement learning in high-dimensional, discrete action spaces is challenging due to the exponential scaling of the joint action space with the number of sub-actions an…
Class-Specific Explainability for Deep Time Series Classifiers
Ramesh Doddaiah, Prathyush Parvatharaju, Elke Rundensteiner +1
Explainability helps users trust deep learning solutions for time series classification. However, existing explainability methods for multi-class time series classifiers focus on o…