  This work is an exploratory research concerned with determining in what way
reinforcement learning can be used to predict optimal PID parameters for a
robot designed for apple harvest. To study this, an algorithm called Advantage
Actor Critic (A2C) is implemented on a simulated robot arm. The simulation
primarily relies on the ROS framework. Experiments for tuning one actuator at a
time and two actuators a a time are run, which both show that the model is able
to predict PID gains that perform better than the set baseline. In addition, it
is studied if the model is able to predict PID parameters based on where an
apple is located. Initial tests show that the model is indeed able to adapt its
predictions to apple locations, making it an adaptive controller.
