Energy systems undergo major transitions to facilitate the large-scale penetration of
renewable energy technologies and improve efficiencies, leading to the integration of many …

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 …

Deep reinforcement learning (RL) has achieved outstanding results in recent years. This has
led to a dramatic increase in the number of applications and methods. Recent works have …

Abstract We introduce Phasic Policy Gradient (PPG), a reinforcement learning framework
which modifies traditional on-policy actor-critic methods by separating policy and value …

Many reinforcement learning (RL) tasks provide the agent with high-dimensional
observations that can be simplified into low-dimensional continuous states. To formalize this …

We present BRIEE, an algorithm for efficient reinforcement learning in Markov Decision
Processes with block-structured dynamics (ie, Block MDPs), where rich observations are …

In goal-reaching reinforcement learning (RL), the optimal value function has a particular
geometry, called quasimetrics structure. This paper introduces Quasimetric Reinforcement …

Robots that can move autonomously and can make intelligent decisions by perceiving their
environments and surrounding objects are known as autonomous mobile robots. Such …

The reinforcement learning (RL) problem is rife with sources of non-stationarity, making it a
notoriously difficult problem domain for the application of neural networks. We identify a …

The low-rank MDP has emerged as an important model for studying representation learning
and exploration in reinforcement learning. With a known representation, several model-free …