Search Results for author: Anwar Walid

Found 13 papers, 2 papers with code

Deep Reinforcement Learning for Traffic Light Control in Intelligent Transportation Systems

no code implementations4 Feb 2023 Xiao-Yang Liu, Ming Zhu, Sem Borst, Anwar Walid

In this paper, we investigate deep reinforcement learning to control traffic lights, and both theoretical analysis and numerical experiments show that the intelligent behavior ``greenwave" (i. e., a vehicle will see a progressive cascade of green lights, and not have to brake at any intersection) emerges naturally a grid road network, which is proved to be the optimal policy in an avenue with multiple cross streets.

reinforcement-learning Reinforcement Learning (RL)

Fair and Efficient Distributed Edge Learning with Hybrid Multipath TCP

no code implementations3 Nov 2022 Shiva Raj Pokhrel, Jinho Choi, Anwar Walid

The bottleneck of distributed edge learning (DEL) over wireless has shifted from computing to communication, primarily the aggregation-averaging (Agg-Avg) process of DEL.

Avg Fairness

Regenerative Particle Thompson Sampling

no code implementations15 Mar 2022 Zeyu Zhou, Bruce Hajek, Nakjung Choi, Anwar Walid

Particle Thompson sampling (PTS) is an approximation of Thompson sampling obtained by simply replacing the continuous distribution by a discrete distribution supported at a set of weighted static particles.

Thompson Sampling

ElegantRL-Podracer: Scalable and Elastic Library for Cloud-Native Deep Reinforcement Learning

1 code implementation11 Dec 2021 Xiao-Yang Liu, Zechu Li, Zhuoran Yang, Jiahao Zheng, Zhaoran Wang, Anwar Walid, Jian Guo, Michael I. Jordan

In this paper, we present a scalable and elastic library ElegantRL-podracer for cloud-native deep reinforcement learning, which efficiently supports millions of GPU cores to carry out massively parallel training at multiple levels.

reinforcement-learning Reinforcement Learning (RL) +1

FinRL-Podracer: High Performance and Scalable Deep Reinforcement Learning for Quantitative Finance

no code implementations7 Nov 2021 Zechu Li, Xiao-Yang Liu, Jiahao Zheng, Zhaoran Wang, Anwar Walid, Jian Guo

Unfortunately, the steep learning curve and the difficulty in quick modeling and agile development are impeding finance researchers from using deep reinforcement learning in quantitative trading.

reinforcement-learning Reinforcement Learning (RL) +1

Emerging Trends in Federated Learning: From Model Fusion to Federated X Learning

no code implementations25 Feb 2021 Shaoxiong Ji, Yue Tan, Teemu Saravirta, Zhiqin Yang, Yixin Liu, Lauri Vasankari, Shirui Pan, Guodong Long, Anwar Walid

Federated learning is a new learning paradigm that decouples data collection and model training via multi-party computation and model aggregation.

Federated Learning Meta-Learning +3

Dynamic Sampling and Selective Masking for Communication-Efficient Federated Learning

no code implementations21 Mar 2020 Shaoxiong Ji, Wenqi Jiang, Anwar Walid, Xue Li

Federated learning (FL) is a novel machine learning setting that enables on-device intelligence via decentralized training and federated optimization.

Federated Learning Image Classification +1

Deep Reinforcement Learning for Intelligent Transportation Systems

no code implementations3 Dec 2018 Xiao-Yang Liu, Zihan Ding, Sem Borst, Anwar Walid

Intelligent Transportation Systems (ITSs) are envisioned to play a critical role in improving traffic flow and reducing congestion, which is a pervasive issue impacting urban areas around the globe.

Management reinforcement-learning +1

Practical Deep Reinforcement Learning Approach for Stock Trading

9 code implementations19 Nov 2018 Xiao-Yang Liu, Zhuoran Xiong, Shan Zhong, Hongyang Yang, Anwar Walid

We explore the potential of deep reinforcement learning to optimize stock trading strategy and thus maximize investment return.

reinforcement-learning Reinforcement Learning (RL)

Transform-Based Multilinear Dynamical System for Tensor Time Series Analysis

no code implementations18 Nov 2018 Weijun Lu, Xiao-Yang Liu, Qingwei Wu, Yue Sun, Anwar Walid

We propose a novel multilinear dynamical system (MLDS) in a transform domain, named $\mathcal{L}$-MLDS, to model tensor time series.

Time Series Time Series Analysis

Multidimensional Data Tensor Sensing for RF Tomographic Imaging

no code implementations13 Dec 2017 Tao Deng, Xiao-Yang Liu, Feng Qian, Anwar Walid

The recently proposed transform-based tensor model is more appropriate for sensory data processing, as it helps exploit the geometric structures of the three-dimensional target and improve the recovery precision.

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