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 …

Exploring rich environments and evaluating one's actions without prior knowledge is
immensely challenging. In this paper, we propose Motif, a general method to interface such …

Reinforcement learning algorithms usually assume that all actions are always available to
an agent. However, both people and animals understand the general link between the …

Temporal abstraction in reinforcement learning (RL), offers the promise of improving
generalization and knowledge transfer in complex environments, by propagating information …

Reinforcement Learning (RL), bolstered by the expressive capabilities of Deep Neural
Networks (DNNs) for function approximation, has demonstrated considerable success in …

Selecting exploratory actions that generate a rich stream of experience for better learning is
a fundamental challenge in reinforcement learning (RL). An approach to tackle this problem …

Automated industries lead to high quality production, lower manufacturing cost and better
utilization of human resources. Robotic manipulator arms have major role in the automation …

The objective of lifelong reinforcement learning (RL) is to optimize agents which can
continuously adapt and interact in changing environments. However, current RL approaches …

Conventional hierarchical reinforcement learning (HRL) relies on discrete options to
represent explicitly distinguishable knowledge, which may lead to severe performance …