Search Results for author: Wenhui Huang

Found 7 papers, 3 papers with code

Learning to Weight Samples for Dynamic Early-exiting Networks

1 code implementation17 Sep 2022 Yizeng Han, Yifan Pu, Zihang Lai, Chaofei Wang, Shiji Song, Junfen Cao, Wenhui Huang, Chao Deng, Gao Huang

Intuitively, easy samples, which generally exit early in the network during inference, should contribute more to training early classifiers.

Meta-Learning

Safe Decision-making for Lane-change of Autonomous Vehicles via Human Demonstration-aided Reinforcement Learning

no code implementations1 Jul 2022 Jingda Wu, Wenhui Huang, Niels de Boer, Yanghui Mo, Xiangkun He, Chen Lv

Decisions made by human subjects in a driving simulator are treated as safe demonstrations, which are stored into the replay buffer and then utilized to enhance the training process of RL.

Autonomous Driving Decision Making +1

Sampling Efficient Deep Reinforcement Learning through Preference-Guided Stochastic Exploration

no code implementations20 Jun 2022 Wenhui Huang, Cong Zhang, Jingda Wu, Xiangkun He, Jie Zhang, Chen Lv

We theoretically prove that the policy improvement theorem holds for the preference-guided $\epsilon$-greedy policy and experimentally show that the inferred action preference distribution aligns with the landscape of corresponding Q-values.

Q-Learning reinforcement-learning

Glance and Focus Networks for Dynamic Visual Recognition

1 code implementation9 Jan 2022 Gao Huang, Yulin Wang, Kangchen Lv, Haojun Jiang, Wenhui Huang, Pengfei Qi, Shiji Song

Spatial redundancy widely exists in visual recognition tasks, i. e., discriminative features in an image or video frame usually correspond to only a subset of pixels, while the remaining regions are irrelevant to the task at hand.

Image Classification Video Recognition

Prioritized Experience-based Reinforcement Learning with Human Guidance for Autonomous Driving

1 code implementation26 Sep 2021 Jingda Wu, Zhiyu Huang, Wenhui Huang, Chen Lv

A novel prioritized experience replay mechanism that adapts to human guidance in the reinforcement learning process is proposed to boost the efficiency and performance of the reinforcement learning algorithm.

Autonomous Driving online learning +1

Learning to drive via Apprenticeship Learning and Deep Reinforcement Learning

no code implementations12 Jan 2020 Wenhui Huang, Francesco Braghin, Zhuo Wang

Therefore, we propose an apprenticeship learning in combination with deep reinforcement learning approach that allows the agent to learn the driving and stopping behaviors with continuous actions.

Robotics

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