GLIDE addresses the challenge of controlling the movement of a fleet of UAVs by using Multi-Agent Deep Reinforcement Learning (MARL). A continuous-time based Proximal Policy Optimization (PPO) algorithm for multi-aGent Learning In Dynamic Environments called GLIDE is implemented. The action control ...
In this project, Greedy Perimeter Stateless Routing (GPSR), Gradient Routing (GRAd), Destination-Sequenced Distance Vector routing (DSDV) and some Reinforcement-Learning based Routing Protocol have been implemented. The following figure illustrates the routing procedure of GRAd and GPSR. More detail inf...
Towards Monocular Vision based ObstacleAvoidance through Deep Reinforcement Learning.pdf added papersUAV Cooperative Control with Stochastic Risk Models.pdf added papers UAV TaskAssignment.pdf added papers UAVSim-Simulator-for-CyberSecurityAnalysis.pdf added papers ...
PyFlyt - UAV Flight Simulator for Reinforcement Learning Comes with Gymnasium and PettingZoo environments built in!View the documentation here!This is a library for testing reinforcement learning algorithms on UAVs. This repo is still under development. We are also actively looking for users and ...
reinforcement-learning uav reward data-rate uav-trajectory Updated Mar 20, 2023 Python ricardo-s-santos / AutoNAV Star 0 Code Issues Pull requests python python-library uav-tracking uav-trajectory Updated Feb 11, 2024 Python Improve this page Add a description, image, and links to th...
Some human-machine systems are designed so that machines (robots) gather and deliver data to remotely located operators (humans) through an interface to ai
git clone https://github.com/dmslab-konkuk/LogisticsEnv.git cd MAACorcd MADDPG edit parameters in main.py (learning parameters) Followed by your OS, select built environment file betweenBuild_WindowsorBuild_Linux(give right path) if your OS is Linux(Ubuntu), before training grant permission is...
unmanned aerial vehicles; UAV; reinforcement learning; Q-learning; deep Q-learning; obstacle avoidance; path planning1. Introduction With the development of integrated circuit technology, rapid growth in computing power has led to a surge in the application of drones. Advances in microcontrollers have...
This paper summarizes in depth the state of the art of aerial swarms, covering both classical and new reinforcement-learning-based approaches for their management. Then, it proposes a hybrid AI system, integrating deep reinforcement learning in a multi-a
For the path planning of interception scenarios, the planned path should satisfy the dynamic constraints of the fixed-wing UAV. In addition, the time of the UAV arriving at the interception point should be reduced to improve the execution efficiency of the interception task. The multi-UAV coopera...