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

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 …

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 present a framework for learning hierarchical policies from demonstrations, using sparse
natural language annotations to guide the discovery of reusable skills for autonomous …

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

Decision-making is challenging in robotics environments with continuous object-centric
states, continuous actions, long horizons, and sparse feedback. Hierarchical approaches …

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 …

Building autonomous machines that can explore open-ended environments, discover
possible interactions and build repertoires of skills is a general objective of artificial …