In accordance with the rapid proliferation of machine learning (ML) and data management,
ML applications have evolved to encompass all engineering disciplines. Owing to the …

The objective of this paper is to examine the use and applications of reinforcement learning
(RL) techniques in the production planning and control (PPC) field addressing the following …

First-person view (FPV) drone racing is a televised sport in which professional competitors
pilot high-speed aircraft through a 3D circuit. Each pilot sees the environment from the …

The ambition of harnessing the quantum for computation is at odds with the fundamental
phenomenon of decoherence. The purpose of quantum error correction (QEC) is to …

STABLE-BASELINES3 provides open-source implementations of deep reinforcement
learning (RL) algorithms in Python. The implementations have been benchmarked against …

In this work we identify the dormant neuron phenomenon in deep reinforcement learning,
where an agent's network suffers from an increasing number of inactive neurons, thereby …

With the development of deep representation learning, the domain of reinforcement learning
(RL) has become a powerful learning framework now capable of learning complex policies …

Cellular networks are undergoing a radical transformation toward disaggregated, fully
virtualized, and programmable architectures with increasingly heterogeneous devices and …

As buildings account for approximately 40% of global energy consumption and associated
greenhouse gas emissions, their role in decarbonizing the power grid is crucial. The …

Deep reinforcement learning (DRL) integrates the feature representation ability of deep
learning with the decision-making ability of reinforcement learning so that it can achieve …