Reinforcement learning is a powerful tool in artificial intelligence in which virtual or physical agents learn to optimize their decision making to achieve longterm goals. In some cases, this machine learning approach can save programmers time, outperform existing controllers, reach superhuman performance, and continually adapt to changing conditions. This book argues that these successes show reinforcement learning can be adopted successfully in many different situations, including robot control, sk trading, supply chain optimization, and plant control. However, reinforcement learning has traditionally been limited to applications in virtual environments or simulations in which the setup is already provided. Furthermore, experimentation may be completed for an almost limitless number of attempts riskfree. In many reallife tasks, applying reinforcement learning is not as simple as (1) data is not in the correct form for reinforcement learning, (2) data is scarce, and (3) automation has limitations in the realworld. Therefore, this book is written to help academics, domain specialists, and data enthusiast alike to understand the basic principles of applying reinforcement learning to realworld problems. This is achieved by focusing on the process of taking practical examples and modeling standard data into the correct form required to then apply basic agents. To further assist with readers gaining a deep and grounded understanding of the approaches, the book shows handcalculated examples in full and then how this can be achieved in a more automated manner with code. For decision makers who are interested in reinforcement learning as a solution but are not technically proficient we include simple, nontechnical examples in the introduction and case studies section. These provide context of what reinforcement learning offer but also the challenges and risks associated with applying it in practice. Specifically, the book illustrates the differences between reinforcement learning and other machine learning approaches as well as how wellknown companies have found success using the approach to their problems.
Reinforcement learning is a powerful tool in artificial intelligence in which virtual or physical agents learn to optimize their decision making to achieve longterm goals. In some cases, this machine learning approach can save programmers time, outperform existing controllers, reach superhuman performance, and continually adapt to changing conditions. This book argues that these successes show reinforcement learning can be adopted successfully in many different situations, including robot control, sk trading, supply chain optimization, and plant control. However, reinforcement learning has traditionally been limited to applications in virtual environments or simulations in which the setup is already provided. Furthermore, experimentation may be completed for an almost limitless number of attempts riskfree. In many reallife tasks, applying reinforcement learning is not as simple as (1) data is not in the correct form for reinforcement learning, (2) data is scarce, and (3) automation has limitations in the realworld. Therefore, this book is written to help academics, domain specialists, and data enthusiast alike to understand the basic principles of applying reinforcement learning to realworld problems. This is achieved by focusing on the process of taking practical examples and modeling standard data into the correct form required to then apply basic agents. To further assist with readers gaining a deep and grounded understanding of the approaches, the book shows handcalculated examples in full and then how this can be achieved in a more automated manner with code. For decision makers who are interested in reinforcement learning as a solution but are not technically proficient we include simple, nontechnical examples in the introduction and case studies section. These provide context of what reinforcement learning offer but also the challenges and risks associated with applying it in practice. Specifically, the book illustrates the differences between reinforcement learning and other machine learning approaches as well as how wellknown companies have found success using the approach to their problems.
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