Cooperative Robot Control in Flexible Manufacturing Cells: Centralized vs. Distributed Approaches This paper introduces a novel approach for the control of flexible manufacturing units by means of cooperatively interacting industrial robots. For fast adoption to the actual production requirements, we embed a learning module into the manufacturing cell. This learning module allows the robots to learn to solve the given task with respect to a given optimization objective. Simultaneously, the robots learn to efficiently cooperate and find an optimal collective behavior while solving the task. To this end, we develop two different control algorithms based on reinforcement learning. The first approach is based on a centralized agent which coordinates the learning behavior of the whole manufacturing cell. In the second approach, a learning agent is assigned to each robot allowing for more flexibility and reducing the stateaction space of the reinforcement learning problem at hand. The approaches are applied to a laboratory testbed using two cooperating industrial robots which should learn to optimize the throughput of the manufacturing cell. A comparison of both approaches shows the improved performance of the decentralized learning agents compared to the centralized one both in goal achievement and learning speed.

MultiAgent Reinforcement Learning Tool for Job Shop Scheduling Problems The emergence of Industry 4.0 allows for new approaches to solve industrial problems such as the Job Shop Scheduling Problem. It has been demonstrated that MultiAgent Reinforcement Learning approaches are highly promising to handle complex scheduling scenarios. In this work we propose a user friendly MultiAgent Reinforcement Learning tool, more appealing for industry. It allows the users to interact with the learning algorithms in such a way that all the constraints in the production floor are carefully included and the objectives can be adapted to real world scenarios. The user can either keep the best schedule obtained by a QLearning algorithm or adjust it by fixing some operations in order to meet certain constraints, then the tool will optimize the modified solution respecting the user preferences using two possible alternatives. These alternatives are validated using ORLibrary benchmarks, the experiments show that the modified QLearning algorithm is able to obtain the best results.