Markov Models
Sample Complexity of Goal-Conditioned Hierarchical Reinforcement Learning
HRL lead to improved sample complexity, and how much of an improvement can it provide? Theoretical work on sample-complexity bound in Machine Learning has been integral to the development of the field.
Policy Gradient for Rectangular Robust Markov Decision Processes Anonymous Author(s) Affiliation Address email
We provide a closed-form expression for the worst occupation measure.
Learning Adversarial Low-rank Markov Decision Processes with Unknown Transition and Full-information Feedback
In this work, we study the low-rank MDPs with adversarially changed losses in the full-information feedback setting.
Learning Adversarial Low-rank Markov Decision Processes with Unknown Transition and Full-information Feedback
In this work, we study the low-rank MDPs with adversarially changed losses in the full-information feedback setting.
One Demonstration is Enough to Learn Robot Policies
Sequential decision-making problems typically require significant human supervision and data.
A Supplementary Material This Supplementary Material section provides additional details and discussions to complement our
C Results in ALFRED's T ask and New T asks