Comment by dragoman1993

As a researcher in the autonomous robotics space, there is a lot to gain in RL over PIDs and manual flight.

The delta between what is possible with current autonomous flight missions and manual FPV style flight is by having a brain on board that can dynamically adapt to a changing environment. There are a finite amount of PID profiles for each steadystate solution that a researcher can preprepare for. But RL allows an overarching heuristic to transiently alter the PIDs depending on the changing environment.

We use PIDs because analyzing robotics platforms as seeking a steadystate dramatically simplifies the math needed to where its computationally possible for us to solve for a situation.

We use RL in systems that have continuously changing environments with transient solution spaces that are easier to model in hyperspace with a RL model.

Take for example platforms that have tiltrotors. They ideally have a minimum of 3 PID profiles for flying. One when it best fits a multirotor profile. A second when it is transitioning from multirotor to fixed wing flight, and a third for when fixed wing flight is established. What happens when the researcher has a need to fly in the transition state, or subconfigurations of the states? How many PID profiles are you looking to think of and train for? This is where RL has dividends.