In this work, we introduce a new variant of online gradient descent, whi...
In online reinforcement learning (RL), instead of employing standard
str...
While Reinforcement Learning (RL) aims to train an agent from a reward
f...
This work develops new algorithms with rigorous efficiency guarantees fo...
This paper provides a theoretical study of deep neural function approxim...
Abstract object properties and their relations are deeply rooted in huma...
Imitation learning (IL) is a popular paradigm for training policies in
r...
We study the inverse reinforcement learning (IRL) problem under the
tran...