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Deep reinforcement learning (DRL) combines deep learning (DL) with a reinforcement learning (RL) architecture. It has been able to perform a wide range of complex decision-making tasks that were previously intractable for a machine. Moreover, DRL has contributed to the recent great successes in artificial intelligence (AI) like AlphaGo and OpenAI Five. Indeed, DRL has opened up many exciting avenues to explore in a variety of domains such as healthcare, robotics, smart grids, and finance. Divided into three main parts, this book provides a comprehensive and selfcontained introduction to DRL. The first part introduces the foundations of DL, RL and widely used DRL methods and then discusses their implementations, which includes Chaps. 1–6. The second part covers selected DRL research topics in Chaps. 7–12, which are useful for those would like to specialize in DRL research. To help readers gain a deep …