Give value to actions instead to angles and then decide on the actions.
Simplified experiment to the point where one can see that the multiscale qlearning of both place and hd to action interferes with the taxic navigation system after a few iterations of eating.
Possible solutions: implement continuous q-learning or maybe just tweak negative rewards.
Experimenting with no multilayer and HD-Place-Action learning. Still see some circles and weird behavior, but has some directional behaviour towards the feeders.
Added proportional controller. Seems to work while there is flashing, but learns to go straight when there is none.
Added goal deciders at the beginning and end of the epoch, trying to fix the jump in value after it gets to the food. Discovered that it occurs when the robot returns to a known intention.
Lowering the width of the place cells diminishes the effect of circling around after getting to the food.
Tested with several layers of both and seems to work, must get it from stucking sometimes, but once on course, regains control. Only tested training.
Changed unflash feeder to fix problem when going to recall. First experiment of the HD, PC, and intention model working. The training phase was completed with no help, with a wall avoidance behavior taking great part in making it reach the feeder. The testing phase failed because one feeder kept flashing.
Implemented many joint states, seems to work
Added improvement for efficiency sake.
Fixed a bug in the many state activation. Experiments show going around the wall because straight action weights way more