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A PPO-based DRL Auto-Tuning Nonlinear PID Drone Controller for Robust Autonomous Flights
This project aims to revolutionize drone flight control by implementing a nonlinear Deep Reinforcement Learning (DRL) agent as a replacement for traditional linear Proportional Integral Derivative (PID) controllers.
Towards a High-Performance Object Detector: Insights from Drone Detection Using ViT and CNN-based Deep Learning Models
Accurate drone detection is strongly desired in drone collision avoidance, drone defense and autonomous Unmanned Aerial Vehicle (UAV) self-landing.
Digital twin brain: a bridge between biological intelligence and artificial intelligence
In recent years, advances in neuroscience and artificial intelligence have paved the way for unprecedented opportunities for understanding the complexity of the brain and its emulation by computational systems.
Plume: A Framework for High Performance Deep RL Network Controllers via Prioritized Trace Sampling
First, we identify the critical features that determine the behavior of the traces.
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