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Found 9 papers, 0 papers with code
A Safe Reinforcement Learning Algorithm for Supervisory Control of Power Plants
In power plant control, one often needs to obtain a precise representation of the system dynamics and carefully design the control scheme accordingly.
Real-time Bandwidth Estimation from Offline Expert Demonstrations
In this work, we tackle the problem of bandwidth estimation (BWE) for real-time communication systems; however, in contrast to previous works, we leverage the vast efforts of prior heuristic-based BWE methods and synergize these approaches with deep learning-based techniques.
Numerical evidence against advantage with quantum fidelity kernels on classical data
We show that the general-purpose hyperparameter tuning techniques proposed to improve the generalization of quantum kernels lead to the kernel becoming well-approximated by a classical kernel, removing the possibility of quantum advantage.
Multifidelity Reinforcement Learning with Control Variates
The proposed estimator, which is based on the method of control variates, is used to design a multifidelity Monte Carlo RL (MFMCRL) algorithm that improves the learning of the agent in the high-fidelity environment.
Data-Driven Random Access Optimization in Multi-Cell IoT Networks with NOMA
To enable a capacity-optimal network, a novel formulation of random channel access management is proposed, in which the transmission probability of each IoT device is tuned to maximize the geometric mean of users' expected capacity.
A Gradient-Aware Search Algorithm for Constrained Markov Decision Processes
In this brief, we first prove that the optimization objective in the dual linear program of a finite CMDP is a piece-wise linear convex function (PWLC) with respect to the Lagrange penalty multipliers.
Constrained Deep Reinforcement Learning for Energy Sustainable Multi-UAV based Random Access IoT Networks with NOMA
In this paper, we apply the Non-Orthogonal Multiple Access (NOMA) technique to improve the massive channel access of a wireless IoT network where solar-powered Unmanned Aerial Vehicles (UAVs) relay data from IoT devices to remote servers.
Learning to Optimize Variational Quantum Circuits to Solve Combinatorial Problems
Proposed recently, the Quantum Approximate Optimization Algorithm (QAOA) is considered as one of the leading candidates for demonstrating quantum advantage in the near term.
Reinforcement-Learning-Based Variational Quantum Circuits Optimization for Combinatorial Problems
The Quantum Approximate Optimization Algorithm (QAOA) is arguably one of the leading quantum algorithms that can outperform classical state-of-the-art methods in the near term.
