A novel deep reinforcement learning framework with human activation function to control quadrotor

Key Points

• The framework achieves superior control accuracy for quadrotors, enhancing their operational stability and response.
• Key performance metrics show a 30% increase in control efficiency in simulations compared to traditional methods.
• This analysis utilizes deep reinforcement learning techniques to optimize the control algorithms for quadrotor navigation.
• The findings highlight potential advancements in aerial robotics, calling for further exploration in real-world applications.