Hierarchical Reinforcement Learning (HRL) enables autonomous decomposition of
challenging long-horizon decision-making tasks into simpler subtasks. During the past …

In recent years, deep artificial neural networks (including recurrent ones) have won
numerous contests in pattern recognition and machine learning. This historical survey …

Sequential decision making, commonly formalized as Markov Decision Process (MDP)
optimization, is an important challenge in artificial intelligence. Two key approaches to this …

Learning goal-directed behavior in environments with sparse feedback is a major challenge
for reinforcement learning algorithms. One of the key difficulties is insufficient exploration …

Temporal abstraction is key to scaling up learning and planning in reinforcement learning.
While planning with temporally extended actions is well understood, creating such …

We describe a framework for multitask deep reinforcement learning guided by policy
sketches. Sketches annotate tasks with sequences of named subtasks, providing information …

We consider the problem of constructing abstract representations for planning in high-
dimensional, continuous environments. We assume an agent equipped with a collection of …

Reinforcement learning (RL) allows an agent to solve sequential decision-making problems
by interacting with an environment in a trial-and-error fashion. When these environments are …

Hierarchical agents have the potential to solve sequential decision making tasks with
greater sample efficiency than their non-hierarchical counterparts because hierarchical …

Abstract Representation learning and option discovery are two of the biggest challenges in
reinforcement learning (RL). Proto-value functions (PVFs) are a well-known approach for …