The deep RL controller can be further improved by incorporating additional sensory information, such as force/torque sensors, to enhance the system's feedback and control capabilities. Force/torque sensors can provide direct measurements of interaction forces between the drones and the transported objects, enabling more precise and responsive control actions. By integrating sensor feedback into the deep RL algorithm, the controller can adapt to varying external forces and disturbances, improving the system's stability and performance.
Exploring other RL algorithms beyond SAC can also lead to enhancements in the controller's learning efficiency and generalization capabilities. Algorithms like Trust Region Policy Optimization (TRPO), Proximal Policy Optimization (PPO), or Twin Delayed Deep Deterministic Policy Gradient (TD3) offer different trade-offs in terms of sample efficiency, exploration-exploitation balance, and stability. By experimenting with alternative RL algorithms, the controller can potentially achieve better convergence speed, robustness, and performance in challenging environments.
Additionally, incorporating advanced techniques such as curriculum learning, ensemble methods, or meta-learning can further enhance the deep RL controller's adaptability and learning capabilities. These approaches can help the controller efficiently handle complex tasks, generalize across different scenarios, and improve its overall performance in real-world applications.