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

Autotuning PID control using Actor-Critic Deep Reinforcement Learning