… application to continuous control, we augment the basic random search method with three
simple … However, our goal was to minimize the amount of tuning required, and thus we opted …

… PID controllers take PI form, this paper will focus on PI tuning. The proposed approach can
be extended to a full PID controller by … This study uses DeltaV as an example to illustrate the …

… Reinforcement Learning (RL)-based control approach that uses a combination of a Deep
Q-Learning … -based optimal tuning technique was employed to build fuzzy controllers for the …

… for certain types of problems the latter approach, of which reinforcement learning is an
example, can yield faster, more reliable learning. Using several control problems as examples, we …

… of the algorithm and control law. At the core of our approach is the use of a PID controller
as the trainable RL policy. In addition to its simplicity, this approach has several appealing …

… into process control applications. The paper starts by providing an introduction to different
reinforcement learning algorithms. Then, recent successes of RL applications across different …

… Reinforcement learning (RL) algorithms find applications in inventory control, recommender
… That is, the number of parameters to be tuned will be O(MN ). The advantage of using the …

… controller for all iterations and also requires careful tuning of the … the kind we use in LQR,
was also sufficient to control these … : Can simple random search find linear controllers for these …

… the controller’s suggested actions or the learned policy. This can avoid manually tuning
parameters to decide when and how to use … We find these convolutional layers to be typically …

… a different approach and instead search … controller tuning experiments can be leveraged
to increase the efficiency of subsequent experiments. In particular, we consider the application …