yyds-xtt Goto Github PK

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当夜空不再留恋恒星，他会变成流星。

adaptive-federated-learning

Code for paper "Adaptive Federated Learning in Resource Constrained Edge Computing Systems"

adarl-code

Implementation codes and datasets used in ICLR'22 Spotlight paper AdaRL: What, Where, and How to Adapt in Transfer Reinforcement Learning.

aflddpg

* Wu Q, Wang S, Fan P, et al. Deep Reinforcement Learning Based Vehicle Selection for Asynchronous Federated Learning Enabled Vehicular Edge Computing[J]. arXiv preprint arXiv:2304.02832, 2023. 链接： https://arxiv.org/abs/2304.02832 代码： https://github.com/qiongwu86/AFLDDPG

agentformer

[ICCV 2021] Official PyTorch Implementation of "AgentFormer: Agent-Aware Transformers for Socio-Temporal Multi-Agent Forecasting".

agequeueing

A queueing simulator implemented on Python

ai-optimizer

The next generation deep reinforcement learning tookit

allocation-policy-ddpg

arel

Pytorch implementation of AREL

astgcn-r-pytorch

Attention Based Spatial-Temporal Graph Convolutional Networks for Traffic Flow Forecasting, AAAI 2019, pytorch version

asynchronous_impala_ppo

Multi-Agent Deep Reinforcement Learning by using Asynchronous & Impala Proximal Policy Optimization in Pytorch with some explanation

awesome-deep-learning-papers-for-search-recommendation-advertising

Awesome Deep Learning papers for industrial Search, Recommendation and Advertising. They focus on Embedding, Matching, Ranking (CTR and CVR prediction), Post Ranking, Multi-task Learning, Graph Neural Networks, Transfer Learning, Reinforcement Learning, Self-supervised Learning and so on.

big-data-learning

bilevel-optimization-in-coordination-game

code implementation for 'Bi-level Actor-Critic for Multi-agent Coordination'(AAAI2020)

bilstm_basedfederatedlearning

基于Bilastm联邦学习机制的任务预测的MEC环境下的计算卸载算法

blockseq

Code for 'Blockwise Sequential Model Learning for Partially Observable Reinforcement Learning' (AAAI 2022)

bnb_matlab_codes

[10] Multi-Objective Optimization for Adaptive Offloading in Distributed Multi-User MIMO Cell-Free 6G Networks 作者：Zhou W, Xu Y, Li C. 出处：IEEE Internet of Things Journal, 2022 摘要：通过将一些计算任务卸载到边缘服务器，边缘计算可以帮助减轻移动用户日益增加的计算负担，提高用户的体验质量。在本文中，我们研究了具有多个构建基带单元(BBU)的支持边缘计算的多用户无细胞(CF)多输入多输出(MIMO)网络中的计算卸载。联合优化用户关联、传输协方差、CPU周期频率和分配带宽以最小化两个目标：移动设备(MD)的能耗和任务的执行

cache-allocation-project

The purpose of this project is to implement machine learning methods to study resource allocation problems, that is how to share limited resources out among several agents.

causalmarl

cc-

cdrl

Collaborative Deep Reinforcement Learning

chengdu_bss

The location of the base station in the city of Chengdu

chengdu_cab_trajectory

chengdu_taxi_track

Chengdu Taxi Track

comix

computation-offloading-in-uav-enabled-edge-compuing-a-stackelberg-game-approach

source code of the paper "Computation offloading in UAV-enabled Edge Computing: A Stackelberg Game Approach" published in SENSORS

computationoffloadingrl

Matlab project for fairspace

computing_offloading

conference-accepted-paper-list

Some Conferences' accepted papers list (e.g., AAAI, IJCAI, KDD)

cooperative-caching-in-vec-based-on-asynchronous-fl-and-drl

[8] Mobility-Aware Cooperative Caching in Vehicular Edge Computing Based on Asynchronous Federated and Deep Reinforcement Learning 作者：Wu Q, Zhao Y, Fan Q, et al. 出处：IEEE Journal of Selected Topics in Signal Processing, 2023 摘要：车载边缘计算(VEC)可以在路边单元(RSU)中学习和缓存最受车载用户(VU)欢迎的内容，以支持实时车载应用。联邦学习(FL)可以通过共享车辆的本地模型而不是数据来保护VU的隐私。在传统的FL中，全局模型通过聚合所有车辆的本地模型来定期更新