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Found 11 papers, 0 papers with code
Optimizing human-interpretable dialog management policy using Genetic Algorithm
Automatic optimization of spoken dialog management policies that are robust to environmental noise has long been the goal for both academia and industry.
Wasserstein Robust Reinforcement Learning
Reinforcement learning algorithms, though successful, tend to over-fit to training environments hampering their application to the real-world.
System Design and Analysis for Energy-Efficient Passive UAV Radar Imaging System using Illuminators of Opportunity
Then, a set of mission performance evaluators is established to quantitatively assess the capability of the system in a comprehensive manner, including UAV navigation, passive SAR imaging and communication.
Efficient Semi-Implicit Variational Inference
In this paper, we propose CI-VI an efficient and scalable solver for semi-implicit variational inference (SIVI).
Learning to Safely Exploit a Non-Stationary Opponent
On the other hand, overfitting to an opponent (i. e., exploiting only one specific type of opponent) makes the learning player easily exploitable by others.
Reinforcement Learning in Presence of Discrete Markovian Context Evolution
We consider a context-dependent Reinforcement Learning (RL) setting, which is characterized by: a) an unknown finite number of not directly observable contexts; b) abrupt (discontinuous) context changes occurring during an episode; and c) Markovian context evolution.
NAPA: Intermediate-level Variational Native-pulse Ansatz for Variational Quantum Algorithms
Therefore, we propose NAPA, a native-pulse ansatz generator framework for VQAs.
Demonstration of Robust and Efficient Quantum Property Learning with Shallow Shadows
This combined theoretical and experimental analysis positions the robust shallow shadow protocol as a scalable, robust, and sample-efficient protocol for characterizing quantum states on current quantum computing platforms.
Graph Learning for Parameter Prediction of Quantum Approximate Optimization Algorithm
In recent years, quantum computing has emerged as a transformative force in the field of combinatorial optimization, offering novel approaches to tackling complex problems that have long challenged classical computational methods.
