Hierarchical Reinforcement Learning

Counting Reward Automata: Sample Efficient Reinforcement Learning Through the Exploitation of Reward Function Structure

We present counting reward automata—a finite state machine variant capable of modelling any reward function expressible as a …

Tristan Bester, Benjamin Rosman, Steven James, Geraud Nangue Tasse

Hierarchical Reinforcement Learning with AI Planning Models

Deep Reinforcement Learning (DRL) has shown breakthroughs in solving challenging problems, such as pixel-based games and continuous …

Junkyu Lee, Michael Katz, Don Joven Agravante, Miao Liu, Geraud Nangue Tasse, Tim Klinger, Shirin Sohrabi

Learning Options from Demonstration using Skill Segmentation

We present a method for learning options from segmented demonstration trajectories. The trajectories are first segmented into skills …

Matthew Cockcroft, Shahil Mawjee, Steven James, Pravesh Ranchod

Hierarchy Through Composition with Multitask LMDPs

Hierarchical architectures are critical to the scalability of reinforcement learning methods. Most current hierarchical frameworks …

Andrew Saxe, Adam Earle, Benjamin Rosman