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Found 30 papers, 6 papers with code
A review on modelling, evaluation, and optimization of cyber-physical system reliability
The aim of this study is to present an overview of current research on modelling, evaluation, and optimization methods for improving the reliability of Cyber-Physical System (CPS).
Integrated Energy Management for Operational Cost Optimization in Community Microgrids
This study presents an integrated energy management strategy for cost optimization in multi-energy community microgrids (MGs).
Cost-Effective Design of Grid-tied Community Microgrid
This study aims to develop a cost-effective microgrid design that optimally balances the economic feasibility, reliability, efficiency, and environmental impact in a grid-tied community microgrid.
T$^3$-S2S: Training-free Triplet Tuning for Sketch to Scene Generation
This scheme revitalizes the existing ControlNet model, enabling effective handling of multi-instance generations, involving prompt balance, characteristics prominence, and dense tuning.
Fast State Stabilization using Deep Reinforcement Learning for Measurement-based Quantum Feedback Control
Simulations are performed on two-qubit and three-qubit systems, and the results show that our algorithm can successfully stabilize random initial quantum system to the target entangled state, with a convergence time faster than traditional methods such as Lyapunov feedback control and several DRL algorithms with different reward functions.
Adaptive BESS and Grid Setpoints Optimization: A Model-Free Framework for Efficient Battery Management under Dynamic Tariff Pricing
This paper introduces an enhanced framework for managing Battery Energy Storage Systems (BESS) in residential communities.
Online Planning of Power Flows for Power Systems Against Bushfires Using Spatial Context
Our model assumptions are verified by the real bushfire data from NSW, Australia, and we apply our model to two power systems to illustrate its applicability.
Learning Informative Latent Representation for Quantum State Tomography
QST aims to recover the density matrix or the properties of the quantum state from the measured frequencies.
Tomography of Quantum States from Structured Measurements via quantum-aware transformer
Quantum state tomography (QST) is the process of reconstructing the state of a quantum system (mathematically described as a density matrix) through a series of different measurements, which can be solved by learning a parameterized function to translate experimentally measured statistics into physical density matrices.
Auxiliary Task-based Deep Reinforcement Learning for Quantum Control
Due to its property of not requiring prior knowledge of the environment, reinforcement learning has significant potential for quantum control problems.
Model-free Quantum Gate Design and Calibration using Deep Reinforcement Learning
The proposed framework relies only on the measurement at the end of the control process and offers the ability to find the optimal control policy without access to quantum systems during the learning process.
Deep Reinforcement Learning
Model free quantum gate design
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A Dirichlet Process Mixture of Robust Task Models for Scalable Lifelong Reinforcement Learning
We use a Dirichlet process mixture to model the non-stationary task distribution, which captures task relatedness by estimating the likelihood of task-to-cluster assignments and clusters the task models in a latent space.
Efficient Bayesian Policy Reuse with a Scalable Observation Model in Deep Reinforcement Learning
Second, BPR algorithms usually require numerous samples to estimate the probability distribution of the tabular-based observation model, which may be expensive and even infeasible to learn and maintain, especially when using the state transition sample as the signal.
Depthwise Convolution for Multi-Agent Communication with Enhanced Mean-Field Approximation
In this paper, we propose a new method based on local communication learning to tackle the multi-agent RL (MARL) challenge within a large number of agents coexisting.
Simultaneous estimation of parameters and the state of an optical parametric oscillator system
In this paper, we consider the filtering problem of an optical parametric oscillator (OPO).
Path Planning for Cellular-Connected UAV: A DRL Solution with Quantum-Inspired Experience Replay
In cellular-connected unmanned aerial vehicle (UAV) network, a minimization problem on the weighted sum of time cost and expected outage duration is considered.
Residual Tensor Train: A Quantum-inspired Approach for Learning Multiple Multilinear Correlations
We prove that such a rule is much more relaxed than that of TT, which means ResTT can easily address the vanishing and exploding gradient problem that exists in the existing TT models.
Rule-Based Reinforcement Learning for Efficient Robot Navigation with Space Reduction
In this paper, we focus on efficient navigation with the RL technique and combine the advantages of these two kinds of methods into a rule-based RL (RuRL) algorithm for reducing the sample complexity and cost of time.
Bayesian adversarial multi-node bandit for optimal smart grid protection against cyber attacks
The cybersecurity of smart grids has become one of key problems in developing reliable modern power and energy systems.
Deep Reinforcement Learning with Quantum-inspired Experience Replay
In this paper, a novel training paradigm inspired by quantum computation is proposed for deep reinforcement learning (DRL) with experience replay.
Expectation Synchronization Synthesis in Non-Markovian Open Quantum Systems
In this article, we investigate the problem of engineering synchronization in non-Markovian quantum systems.
Quantum Physics
Curriculum-based Deep Reinforcement Learning for Quantum Control
Deep reinforcement learning has been recognized as an efficient technique to design optimal strategies for different complex systems without prior knowledge of the control landscape.
Instance Weighted Incremental Evolution Strategies for Reinforcement Learning in Dynamic Environments
Instance novelty measures an instance's difference from the previous optimum in the original environment, while instance quality corresponds to how well an instance performs in the new environment.
Quantum Language Model with Entanglement Embedding for Question Answering
Quantum Language Models (QLMs) in which words are modelled as quantum superposition of sememes have demonstrated a high level of model transparency and good post-hoc interpretability.
Lifelong Incremental Reinforcement Learning with Online Bayesian Inference
In this paper, we propose LifeLong Incremental Reinforcement Learning (LLIRL), a new incremental algorithm for efficient lifelong adaptation to dynamic environments.
Intelligent Trajectory Planning in UAV-mounted Wireless Networks: A Quantum-Inspired Reinforcement Learning Perspective
In this paper, we consider a wireless uplink transmission scenario in which an unmanned aerial vehicle (UAV) serves as an aerial base station collecting data from ground users.
On compression rate of quantum autoencoders: Control design, numerical and experimental realization
Quantum autoencoders which aim at compressing quantum information in a low-dimensional latent space lie in the heart of automatic data compression in the field of quantum information.
Fidelity-based Probabilistic Q-learning for Control of Quantum Systems
In this paper, a fidelity-based probabilistic Q-learning (FPQL) approach is presented to naturally solve this problem and applied for learning control of quantum systems.
Learning-based Quantum Robust Control: Algorithm, Applications and Experiments
Numerical results are presented to demonstrate the excellent performance of the improved machine learning algorithm for these two classes of quantum robust control problems.
Quantum reinforcement learning
The state (action) set can be represented with a quantum superposition state and the eigen state (eigen action) can be obtained by randomly observing the simulated quantum state according to the collapse postulate of quantum measurement.
