Pinned Repositories
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
Computing_offloading
DRL-Based-Long-Term-Resource-Planning
Paper publised in TNSM entitled "DRL-Based Long-Term Resource Planning for Task Offloading Policies in Multi-Server Edge Computing Networks"
DRL-for-edge-computing
DRL-MEC
Dynamic Task Software Caching-Assisted Computation Offloading for Multi-Access Edge Computing
DRL-TOBS
Codes for the paper titled Online Joint Task Offloading and Resource Management in Heterogeneous Mobile Edge Environments.
edge-offloading
computation offloading in mobile edge computing using Reinforcement Learning
Game-Theoretic-Deep-Reinforcement-Learning
Code of Paper "Joint Task Offloading and Resource Optimization in NOMA-based Vehicular Edge Computing: A Game-Theoretic DRL Approach", JSA 2022.
Graph-reinforcement-learning-literature
This open source library is available to summarize several years of research papers on graph reinforcement learning for the convenience of researchers
PeerJ-Computer-Science
yyds-xtt's Repositories
yyds-xtt/DRL-TOBS
Codes for the paper titled Online Joint Task Offloading and Resource Management in Heterogeneous Mobile Edge Environments.
yyds-xtt/Game-Theoretic-Deep-Reinforcement-Learning
Code of Paper "Joint Task Offloading and Resource Optimization in NOMA-based Vehicular Edge Computing: A Game-Theoretic DRL Approach", JSA 2022.
yyds-xtt/Computing_offloading
yyds-xtt/Offloading_to_Vehicles_2
yyds-xtt/Multi-Agent-Reinforcement-Learning-in-NOMA-Aided-UAV-Networks-for-Cellular-Offloading
Code for the paper 'Multi-Agent Reinforcement Learning in NOMA-Aided UAV Networks for Cellular Offloading'
yyds-xtt/offloading-optimization-and-resource-optimization-base-on-mobile-edge-computing-net
基于边缘计算网络的卸载优化及资源优化
yyds-xtt/TETC-DQN-DQN-NN
Reinforcement learning-based mobile edge computing and transmission scheduling for video surveillance
yyds-xtt/UAV_RL_Maximize_Throughput
UAV-based MEC to maximize user throughput via reinforcement learning
yyds-xtt/delay-aware-madrl
yyds-xtt/DRL-1
车联网环境
yyds-xtt/marl_transfer
Code for paper 'Learning transferable cooperative behaviors in multi-agent teams' (ICML 2019)
yyds-xtt/task-allocation-auctions
Dynamic decentralized task allocation algorithms for multi-agent systems using auctions and machine learning
yyds-xtt/CUP
yyds-xtt/DHDRL
Distributed Two-tier DRL Framework for Cell-Free Network: Association, Beamforming and Power Allocation
yyds-xtt/EMORL-TCTO
An implementation of EMORL-TCTO algorithm.
yyds-xtt/further
Source code for "Influencing Long-Term Behavior in Multiagent Reinforcement Learning" (NeurIPS 2022)
yyds-xtt/malfoy
yyds-xtt/maro
Multi-Agent Resource Optimization (MARO) platform is an instance of Reinforcement Learning as a Service (RaaS) for real-world resource optimization problems.
yyds-xtt/mec_alloc_rl
User allocation in Mobile Edge Computing Environment using Reinforcement Learning
yyds-xtt/MEC_VER_NEW_VEHICLES
yyds-xtt/mobile-env
An open, minimalist Gym environment for autonomous coordination in wireless mobile networks.
yyds-xtt/Model-Based-MARL
yyds-xtt/modeling
Joint Cache Placement and Request Routing Optimization in Heterogeneous Cellular Networks
yyds-xtt/Multi-Agent-Transformer
yyds-xtt/PerfectDou
[NeurIPS 2022] PerfectDou: Dominating DouDizhu with Perfect Information Distillation
yyds-xtt/Rome_MVs_TRs
The mobile vehicles (MVs) trajectories in the Roman city.
yyds-xtt/tvt2022
Oracle-Guided Deep Reinforcement Learning for Large-Scale Multi-UAVs Flocking and Navigation
yyds-xtt/UARA-DRL
This repository contains the implementation codes of deep reinforcement learning (DRL) in user association and resource allocation in heterogenous networks. The model is basd on deep-Q-network (DQN) and DDQN.
yyds-xtt/UAV-RIS-DRL
3D-Trajectory and Phase-Shift Design for RIS-Assisted UAV Systems Using Deep Reinforcement Learning
yyds-xtt/uav_bs_ctrl
Code implementation of "Cooperative Trajectory Design of Multiple UAV Base Stations with Heterogeneous Graph Neural Networks".