Search Results for author: Yanghe Feng

Found 7 papers, 1 papers with code

PTR-PPO: Proximal Policy Optimization with Prioritized Trajectory Replay

no code implementations7 Dec 2021 Xingxing Liang, Yang Ma, Yanghe Feng, Zhong Liu

In addition, by analyzing the heatmap of priority changes at various locations in the priority memory during training, we find that memory size and rollout length can have a significant impact on the distribution of trajectory priorities and, hence, on the performance of the algorithm.

Dynamic Binary Neural Network by learning channel-wise thresholds

no code implementations8 Oct 2021 Jiehua Zhang, Zhuo Su, Yanghe Feng, Xin Lu, Matti Pietikäinen, Li Liu

The experimental results prove that our method is an effective and straightforward way to reduce information loss and enhance performance of BNNs.

Federated Submodel Averaging

no code implementations16 Sep 2021 Yucheng Ding, Chaoyue Niu, Fan Wu, Shaojie Tang, Chengfei Lv, Yanghe Feng, Guihai Chen

We theoretically proved the convergence rate of FedSubAvg by deriving an upper bound under a new metric called the element-wise gradient norm.

Federated Learning

HpGAN: Sequence Search with Generative Adversarial Networks

no code implementations10 Dec 2020 Mingxing Zhang, Zhengchun Zhou, Lanping Li, Zilong Liu, Meng Yang, Yanghe Feng

Sequences play an important role in many engineering applications and systems.

MRPB 1.0: A Unified Benchmark for the Evaluation of Mobile Robot Local Planning Approaches

1 code implementation1 Nov 2020 Jian Wen, Xuebo Zhang, Qingchen Bi, Zhangchao Pan, Yanghe Feng, Jing Yuan, Yongchun Fang

Local planning is one of the key technologies for mobile robots to achieve full autonomy and has been widely investigated.

Robotics

Deep Multi-View Spatiotemporal Virtual Graph Neural Network for Significant Citywide Ride-hailing Demand Prediction

no code implementations30 Jul 2020 Guangyin Jin, Zhexu Xi, Hengyu Sha, Yanghe Feng, Jincai Huang

Urban ride-hailing demand prediction is a crucial but challenging task for intelligent transportation system construction.

Graph Attention

VMAV-C: A Deep Attention-based Reinforcement Learning Algorithm for Model-based Control

no code implementations24 Dec 2018 Xingxing Liang, Qi. Wang, Yanghe Feng, Zhong Liu, Jincai Huang

Recent breakthroughs in Go play and strategic games have witnessed the great potential of reinforcement learning in intelligently scheduling in uncertain environment, but some bottlenecks are also encountered when we generalize this paradigm to universal complex tasks.

Deep Attention Model-based Reinforcement Learning

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