Simulated the scenario between edge servers and users with a clear graphic interface. Also, implemented the continuous control with Deep Deterministic Policy Gradient (DDPG) to determine the resour…

Reinforcement learning with Proximal Policy Optimization (https://arxiv.org/abs/1707.06347)

This is the official implementation of Multi-Agent PPO (MAPPO).

PyTorch implementation of DQN, AC, ACER, A2C, A3C, PG, DDPG, TRPO, PPO, SAC, TD3 and ....

Using DDPG agent to control UAV system with energy efficiency

part code of paper entitled "battery-constrained federated edge learning in uav-enabled iot for b5g/6g networks"

🎨 ML Visuals contains figures and templates which you can reuse and customize to improve your scientific writing.

使用Drl来解决多智能体卸载问题

基于深度强化学习的部分计算任务卸载延迟优化

It's a implementation about the paper Liang Huang, Suzhi Bi, and Ying-jun Angela Zhang, "Deep Reinforcement Learning for Online Computation Offloading in Wireless Powered Mobile-Edge Computing Netw…

论文UAV relay in VANETs against smart jamming with reinforcement learning DQN版本

During our participation in the Internship Exchange Program, my friend and I collaborated with the guidance of our esteemed supervisor from NTHU.

[TMC 2023] Delay-Sensitive Energy-Efficient UAV Crowdsensing by Deep Reinforcement Learning

Simulation and documentations of Multiple UAV TPC optimization

created an environment of 10*10 grid and 4 UAVs to carry out coverage path planning cooperatively

Code for the paper 'Multi-Agent Reinforcement Learning in NOMA-Aided UAV Networks for Cellular Offloading'

This is the source code of "Efficient training techniques for multi-agent reinforcement learning in combatant tasks".

[JSAC 2018] Energy-Efficient UAV Control for Effective and Fair Communication Coverage: A Deep Reinforcement Learning Approach

Helper scripts and programs for trajectories

Autonomous Navigation of UAV using Reinforcement Learning algorithms.