Normalization techniques are essential for accelerating the training and improving the
generalization of deep neural networks (DNNs), and have successfully been used in various …

This work identifies a common flaw of deep reinforcement learning (RL) algorithms: a
tendency to rely on early interactions and ignore useful evidence encountered later …

Dispatching flexible unmanned aerial vehicles (UAVs) to collect data from distributed
Internet-of-Things devices (IoTDs) is expected to be a promising technology to support time …

There has recently been a surge in research in batch Deep Reinforcement Learning (DRL),
which aims for learning a high-performing policy from a given dataset without additional …

While deep reinforcement learning (DRL) based energy management strategies (EMSs)
have shown potential for optimizing energy utilization in recent years, challenges such as …

Managing numerous distributed energy resources (DERs) within the virtual power plant
(VPP) is challenging due to inaccurate parameters and unknown dynamic characteristics. To …

We propose VRL3, a powerful data-driven framework with a simple design for solving
challenging visual deep reinforcement learning (DRL) tasks. We analyze a number of major …

Reinforcement learning (RL) for continuous control typically employs distributions whose
support covers the entire action space. In this work, we investigate the colloquially known …

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

Atomic-scale manipulation in scanning tunneling microscopy has enabled the creation of
quantum states of matter based on artificial structures and extreme miniaturization of …