1 citations · 2 across the 10 of their papers we have counts for
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Markov Balance Satisfaction Improves Performance in Strictly Batch Offline Imitation Learning
Rishabh Agrawal, Nathan Dahlin, Rahul Jain +1
Imitation learning (IL) is notably effective for robotic tasks where directly programming behaviors or defining optimal control costs is challenging. In this work, we address a sce…
Conditional Kernel Imitation Learning for Continuous State Environments
Rishabh Agrawal, Nathan Dahlin, Rahul Jain +1
Imitation Learning (IL) is an important paradigm within the broader reinforcement learning (RL) methodology. Unlike most of RL, it does not assume availability of reward-feedback.…
Equivalent and Compact Representations of Neural Network Controllers With Decision Trees
Kevin Chang, Nathan Dahlin, Rahul Jain +1
Over the past decade, neural network (NN)-based controllers have demonstrated remarkable efficacy in a variety of decision-making tasks. However, their black-box nature and the ris…