Hypothesis-Driven Skill Discovery for Hierarchical Deep Reinforcement Learning
Caleb Chuck,Supawit Chockchowwat,Scott Niekum,Caleb Chuck,Supawit Chockchowwat,Scott Niekum
Deep reinforcement learning (DRL) is capable of learning high-performing policies on a variety of complex high-dimensional tasks, ranging from video games to robotic manipulation. However, standard DRL methods often suffer from poor sample efficiency, partially because they aim to be entirely problem-agnostic. In this work, we introduce a novel approach to exploration and hierarchical skill learni...
