Discovering Control Scheduler Policies Through Reinforcement Learning and Evolutionary Strategies

This work investigates the viability of using NNs to select an appropriate controller for a dynamic system based on its current state. To this end, this work proposes a method for training a controller-scheduling policy using several learning algorithms, including deep reinforcement learning and evolutionary strategies. The performance of these scheduler-based approaches is evaluated on an inverted pendulum, and the results are compared with those of NNs that operate directly in a continuous action space and a backpropagation-based Control Scheduling Neural Network. The results demonstrate that machine learning can successfully train a policy to choose the correct controller. The findings highlight that evolutionary strategies offer a compelling trade-off between final performance and computational time, making them an efficient alternative among the scheduling methods tested.