Search Results for author: Shuhan Qi

Found 8 papers, 1 papers with code

SVDE: Scalable Value-Decomposition Exploration for Cooperative Multi-Agent Reinforcement Learning

no code implementations16 Mar 2023 Shuhan Qi, Shuhao Zhang, Qiang Wang, Jiajia Zhang, Jing Xiao, Xuan Wang

In this paper, we propose a scalable value-decomposition exploration (SVDE) method, which includes a scalable training mechanism, intrinsic reward design, and explorative experience replay.

Multi-agent Reinforcement Learning reinforcement-learning +3

An Efficient Split Fine-tuning Framework for Edge and Cloud Collaborative Learning

no code implementations30 Nov 2022 Shaohuai Shi, Qing Yang, Yang Xiang, Shuhan Qi, Xuan Wang

To enable the pre-trained models to be fine-tuned with local data on edge devices without sharing data with the cloud, we design an efficient split fine-tuning (SFT) framework for edge and cloud collaborative learning.

Dynamic Contrastive Distillation for Image-Text Retrieval

no code implementations4 Jul 2022 Jun Rao, Liang Ding, Shuhan Qi, Meng Fang, Yang Liu, Li Shen, DaCheng Tao

Although the vision-and-language pretraining (VLP) equipped cross-modal image-text retrieval (ITR) has achieved remarkable progress in the past two years, it suffers from a major drawback: the ever-increasing size of VLP models restricts its deployment to real-world search scenarios (where the high latency is unacceptable).

Contrastive Learning Metric Learning +3

Parameter-Efficient and Student-Friendly Knowledge Distillation

no code implementations28 May 2022 Jun Rao, Xv Meng, Liang Ding, Shuhan Qi, DaCheng Tao

In this paper, we present a parameter-efficient and student-friendly knowledge distillation method, namely PESF-KD, to achieve efficient and sufficient knowledge transfer by updating relatively few partial parameters.

Knowledge Distillation Transfer Learning

Efficient Distributed Framework for Collaborative Multi-Agent Reinforcement Learning

no code implementations11 May 2022 Shuhan Qi, Shuhao Zhang, Xiaohan Hou, Jiajia Zhang, Xuan Wang, Jing Xiao

However, due to the slow sample collection and poor sample exploration, there are still some problems in multi-agent reinforcement learning, such as unstable model iteration and low training efficiency.

reinforcement-learning Reinforcement Learning (RL) +1

Where Does the Performance Improvement Come From? -- A Reproducibility Concern about Image-Text Retrieval

1 code implementation8 Mar 2022 Jun Rao, Fei Wang, Liang Ding, Shuhan Qi, Yibing Zhan, Weifeng Liu, DaCheng Tao

In contrast to previous works, we focus on the reproducibility of the approaches and the examination of the elements that lead to improved performance by pretrained and nonpretrained models in retrieving images and text.

Information Retrieval Retrieval +1

D2CFR: Minimize Counterfactual Regret with Deep Dueling Neural Network

no code implementations26 May 2021 Huale Li, Xuan Wang, Zengyue Guo, Jiajia Zhang, Shuhan Qi

Towards this problem, a recent method, \textit{Deep CFR} alleviates the need for abstraction and expert knowledge by applying deep neural networks directly to CFR in full games.

RLCFR: Minimize Counterfactual Regret by Deep Reinforcement Learning

no code implementations10 Sep 2020 Huale Li, Xuan Wang, Fengwei Jia, Yi-Fan Li, Yulin Wu, Jiajia Zhang, Shuhan Qi

Extensive experimental results on various games have shown that the generalization ability of our method is significantly improved compared with existing state-of-the-art methods.

Decision Making reinforcement-learning +1

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