Currently, the network traffic control systems are mainly composed of the Internet core and
wired/wireless heterogeneous backbone networks. Recently, these packet-switched …

Graph learning is a popular approach for perfor ming machine learning on graph-structured
data. It has revolutionized the machine learning ability to model graph data to address …

Many practical applications of reinforcement learning constrain agents to learn from a fixed
batch of data which has already been gathered, without offering further possibility for data …

Deep reinforcement learning has emerged as a powerful tool for a variety of learning tasks,
however deep nets typically exhibit forgetting when learning multiple tasks in sequence. To …

Abstract Graph Neural Networks (GNNs) have recently received significant research
attention due to their superior performance on a variety of graph-related learning tasks. Most …

Data aggregation techniques can significantly improve vision-based policy learning within a
training environment, eg, learning to drive in a specific simulation condition. However, as on …

Abstract Prioritized Experience Replay (PER) is a deep reinforcement learning technique in
which agents learn from transitions sampled with non-uniform probability proportionate to …

The first part of a two-part series of papers provides a survey on recent advances in Deep
Reinforcement Learning (DRL) applications for solving partially observable Markov decision …

Research on continual learning (CL) mainly focuses on data represented in the Euclidean
space, while research on graph-structured data is scarce. Furthermore, most graph learning …

Experience replay is a technique that allows off-policy reinforcement-learning methods to
reuse past experiences. The stability and speed of convergence of reinforcement learning …