Y Li - arXiv preprint arXiv:1701.07274, 2017 - arxiv.org
… We start with background of machine learning, deep learning and reinforcementlearning. … In deep learning, between input and output layers, we have one or more hidden layers. At …
Off-policy reinforcementlearning (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 reinforcementlearning. … In reinforcementlearning we are typically interested in acting so as to maximize the return. The …
… This includes hierarchical case-based reasoning, hierarchical reinforcementlearning as well as the layeredlearning (LL) paradigm which was introduced for problems in the robotic …
… Furthermore, QAR algorithm is proposed with the aid of reinforcementlearning and QoS-aware reward function, achieving a time-efficient, adaptive, QoS-provisioning packet forwarding…
… classification tasks, eg for reinforcementlearning (RL), has not been extensively studied. We review recent works in the direction to attain Explainable ReinforcementLearning (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 reinforcementlearning 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 …