Deep Reinforcement Learning and Its Industrial Use Cases AI for Real-World Applications Introduction The rapid development of artificial intelligence (AI) has had a significant impact on various industries, including manufacturing, healthcare, finance, and transportation. One of the most promising areas of AI research is deep reinforcement learning, which combines the power of deep learning with the flexibility of reinforcement learning to create more sophisticated and effective algorithms. This book provides an overview of the current state of deep reinforcement learning and its industrial use cases, highlighting the potential benefits and challenges of this technology. Chapter 1: Understanding Deep Reinforcement Learning In this chapter, we explore the fundamentals of deep reinforcement learning, including the history of the field, key concepts such as Q-learning and policy gradients, and the importance of exploration and exploitation trade-offs. We also discuss the limitations of traditional reinforcement learning methods and how deep reinforcement learning addresses these limitations.

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