I am trying to follow this paper and reproduce their works. But I get stuck with some problems and I really hope that some of you can give me some ideas.
So, basically what the paper has done is firstly building hallway models as environments with randomized texture and furniture placement, then control the drone(with a camera) to move around the environments to avoid obstacles. So the trained neural network receives current camera image and should output the next target point in the image area.
My questions are listed below, I would like to run the simulation (training and testing) all by python scripts with tensorflow to use the neural network. 1) I think for this problem I better use Cycles Baking to save the rendered images to the environments. Am I correct? 2) How to control the camera(installed on the drone) movement considering that the command would be a numerical output of a neural network? Using Blender Game Engine (motion actuator) or normal object move command (acts same as keyboard -G). I need to get the distance from the nearest obstacle and judging if a collision happened to update my Q-value. 3) How to save the camera images? I know that normally it's ctrl-F3 but how to do that in python code? Actually, what I am trying to do is kind of like using the flying navigation mode and save the images(I don't know how to save images in that mode I tried ctrl-F3 but didn't work).
Lastly, are there some tutorials about using reinforcement learning (based on camera images) with blender? Because I've tried finding these on Google many times, but seems there is no good related tutorials.
Thanks for you guys' help. Looking forward to the response!