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The quality and performance of components is largely determined by the execution of the manufacturing processes involved. The process result depends -- in addition to the initial state of the component and the process -- on the course of the process. In many manufacturing processes, the course of the process can be decisively determined by manipulated variables that change over time. This work deals with methods to optimize these time-varying quantities under fluctuating process conditions. The quality of components depend to a large extent on the execution of the industrial processes involved in manufacturing. In addition to the initial conditions of the component and the process, the process result depends on the course of the process, which often can be significantly determined by time-varying manipulated variables. Methods for the optimization of these time-dependent quantities with regard to the component quality and depending on process conditions is the subject of this work.

Reinforcement learning. --- Reinforcement learning

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This work aims to develop a method that can reschedule the matrix production in the case of a disruption. For this purpose, different artificial intelligence methods are combined in a novel way. The developed method is validated on a theoretical and a real scheduling case.

Reinforcement learning. --- Reinforcement learning

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This work aims to develop a method that can reschedule the matrix production in the case of a disruption. For this purpose, different artificial intelligence methods are combined in a novel way. The developed method is validated on a theoretical and a real scheduling case.

Reinforcement learning. --- Reinforcement learning

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Reinforcement learning.

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New edition of the bestselling guide to deep reinforcement learning and how it's used to solve complex real-world problems. Revised and expanded to include multi-agent methods, discrete optimization, RL in robotics, advanced exploration techniques, and more Key Features Second edition of the bestselling introduction to deep reinforcement learning, expanded with six new chapters Learn advanced exploration techniques including noisy networks, pseudo-count, and network distillation methods Apply RL methods to cheap hardware robotics platforms Book Description Deep Reinforcement Learning Hands-On, Second Edition is an updated and expanded version of the bestselling guide to the very latest reinforcement learning (RL) tools and techniques. It provides you with an introduction to the fundamentals of RL, along with the hands-on ability to code intelligent learning agents to perform a range of practical tasks. With six new chapters devoted to a variety of up-to-the-minute developments in RL, including discrete optimization (solving the Rubik's Cube), multi-agent methods, Microsoft's TextWorld environment, advanced exploration techniques, and more, you will come away from this book with a deep understanding of the latest innovations in this emerging field. In addition, you will gain actionable insights into such topic areas as deep Q-networks, policy gradient methods, continuous control problems, and highly scalable, non-gradient methods. You will also discover how to build a real hardware robot trained with RL for less than $100 and solve the Pong environment in just 30 minutes of training using step-by-step code optimization. In short, Deep Reinforcement Learning Hands-On, Second Edition, is your companion to navigating the exciting complexities of RL as it helps you attain experience and knowledge through real-world examples. What you will learn Understand the deep learning context of RL and implement complex deep learning models Evaluate RL methods including cross-entropy, DQN, actor-critic, TRPO, PPO, DDPG, D4PG, and others Build a practical hardware robot trained with RL methods for less than $100 Discover Microsoft's TextWorld environment, which is an interactive fiction games platform Use discrete optimization in RL to solve a Rubik's Cube Teach your agent to play Connect 4 using AlphaGo Zero Explore the very latest deep RL research on topics including AI chatbots Discover advanced exploration techniques, including noisy networks and network di...

Reinforcement learning.