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

In this article, we aim to provide a literature review of different formulations and approaches
to continual reinforcement learning (RL), also known as lifelong or non-stationary RL. We …

Reinforcement learning is a learning paradigm for solving sequential decision-making
problems. Recent years have witnessed remarkable progress in reinforcement learning …

Hierarchical reinforcement learning (HRL) is a promising approach to extend traditional
reinforcement learning (RL) methods to solve more complex tasks. Yet, the majority of …

This tutorial surveys neural approaches to conversational AI that were developed in the last
few years. We group conversational systems into three categories:(1) question answering …

Intelligent agents need to select long sequences of actions to solve complex tasks. While
humans easily break down tasks into subgoals and reach them through millions of muscle …

We present relay policy learning, a method for imitation and reinforcement learning that can
solve multi-stage, long-horizon robotic tasks. This general and universally-applicable, two …

Learning to combine control at the level of joint torques with longer-term goal-directed
behavior is a long-standing challenge for physically embodied artificial agents. Intelligent …

This work considers the problem of learning cooperative policies in complex, partially
observable domains without explicit communication. We extend three classes of single …

Abstract We introduce FeUdal Networks (FuNs): a novel architecture for hierarchical
reinforcement learning. Our approach is inspired by the feudal reinforcement learning …