… a layering perspective. Moreover, the key pros and cons of RL applications in different layers
… Section IV reviews up-to-date RL applications from a layering perspective. In Section V, we …

… We start with background of machine learning, deep learning and reinforcement learning. …
In deep learning, between input and output layers, we have one or more hidden layers. At …

Off-policy reinforcement learning (RL) using a fixed offline dataset of logged interactions is
an important consideration in real world applications. This paper studies offline RL using the …

… a layered reference model to help organize related research and engineering efforts. The
presented layered … This paper presents a layered reference model for how RL agents are …

… trast, we believe the value distribution has a central role to play in reinforcement learning. …
In reinforcement learning we are typically interested in acting so as to maximize the return. The …

… This includes hierarchical case-based reasoning, hierarchical reinforcement learning as
well as the layered learning (LL) paradigm which was introduced for problems in the robotic …

… Furthermore, QAR algorithm is proposed with the aid of reinforcement learning and QoS-aware
reward function, achieving a time-efficient, adaptive, QoS-provisioning packet forwarding…

… classification tasks, eg for reinforcement learning (RL), has not been extensively studied.
We review recent works in the direction to attain Explainable Reinforcement Learning (XRL), a …

… are initially processed by several convolutional layers to extract spatiotemporal features, such
… from the convolutional layers is processed by several fully connected layers, which more …

… 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 decisionmaking …