Search Results for author: Yanqiu Wu

Found 7 papers, 2 papers with code

Radio Signal Classification by Adversarially Robust Quantum Machine Learning

no code implementations13 Dec 2023 Yanqiu Wu, Eromanga Adermann, Chandra Thapa, Seyit Camtepe, Hajime Suzuki, Muhammad Usman

Our extensive simulation results present that attacks generated on QVCs transfer well to CNN models, indicating that these adversarial examples can fool neural networks that they are not explicitly designed to attack.

Classification Image Classification +1

Quantum-Inspired Machine Learning: a Survey

no code implementations22 Aug 2023 Larry Huynh, Jin Hong, Ajmal Mian, Hajime Suzuki, Yanqiu Wu, Seyit Camtepe

Quantum-inspired Machine Learning (QiML) is a burgeoning field, receiving global attention from researchers for its potential to leverage principles of quantum mechanics within classical computational frameworks.

Quantum Machine Learning

Spatio-temporal Incentives Optimization for Ride-hailing Services with Offline Deep Reinforcement Learning

no code implementations6 Nov 2022 Yanqiu Wu, Qingyang Li, Zhiwei Qin

Motivated by this observation, we make an attempt to optimize the distribution of demand to handle this problem by learning the long-term spatio-temporal values as a guideline for pricing strategy.

reinforcement-learning Reinforcement Learning (RL)

Aggressive Q-Learning with Ensembles: Achieving Both High Sample Efficiency and High Asymptotic Performance

no code implementations17 Nov 2021 Yanqiu Wu, Xinyue Chen, Che Wang, Yiming Zhang, Keith W. Ross

In particular, Truncated Quantile Critics (TQC) achieves state-of-the-art asymptotic training performance on the MuJoCo benchmark with a distributional representation of critics; and Randomized Ensemble Double Q-Learning (REDQ) achieves high sample efficiency that is competitive with state-of-the-art model-based methods using a high update-to-data ratio and target randomization.

Continuous Control Q-Learning +1

BAIL: Best-Action Imitation Learning for Batch Deep Reinforcement Learning

1 code implementation NeurIPS 2020 Xinyue Chen, Zijian Zhou, Zheng Wang, Che Wang, Yanqiu Wu, Keith Ross

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 interactions with the environment.

Imitation Learning Q-Learning +2

Striving for Simplicity and Performance in Off-Policy DRL: Output Normalization and Non-Uniform Sampling

3 code implementations ICML 2020 Che Wang, Yanqiu Wu, Quan Vuong, Keith Ross

We aim to develop off-policy DRL algorithms that not only exceed state-of-the-art performance but are also simple and minimalistic.

Continuous Control

Towards Simplicity in Deep Reinforcement Learning: Streamlined Off-Policy Learning

no code implementations25 Sep 2019 Che Wang, Yanqiu Wu, Quan Vuong, Keith Ross

The field of Deep Reinforcement Learning (DRL) has recently seen a surge in the popularity of maximum entropy reinforcement learning algorithms.

Continuous Control reinforcement-learning +1

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