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 …

Autonomous robots facing a diversity of open environments and performing a variety of tasks
and interactions need explicit deliberation in order to fulfill their missions. Deliberation is …

Deep reinforcement learning is the combination of reinforcement learning (RL) and deep
learning. This field of research has been able to solve a wide range of complex …

We propose a lifelong learning system that has the ability to reuse and transfer knowledge
from one task to another while efficiently retaining the previously learned knowledge-base …

In recent years there is a growing interest in using deep representations for reinforcement
learning. In this paper, we present a methodology and tools to analyze Deep Q-networks …

Learning, planning, and representing knowledge at multiple levels of temporal abstraction
are key, longstanding challenges for AI. In this paper we consider how these challenges can …

This paper presents a new approach to hierarchical reinforcement learning based on
decomposing the target Markov decision process (MDP) into a hierarchy of smaller MDPs …

Planning under uncertainty is a central problem in the study of automated sequential
decision making, and has been addressed by researchers in many different fields, including …

Human behavior has long been recognized to display hierarchical structure: actions fit
together into subtasks, which cohere into extended goal-directed activities. Arranging …

In lifelong reinforcement learning, agents must effectively transfer knowledge across tasks
while simultaneously addressing exploration, credit assignment, and generalization. State …