1 Introduction to deep reinforcement learning

 

In this chapter

  • You learn what deep reinforcement learning is and how it is different from other machine learning approaches.
  • You learn about the recent progress in deep reinforcement learning and what it can do for a variety of problems.
  • You know what to expect from this book, and how to get the most out of it.

I visualize a time when we will be to robots what dogs are to humans, and I'm rooting for the machines.

— Claude Shannon, Father of the Information Age and contributor to the field of Artificial Intelligence

Humans naturally pursue feelings of happiness. From picking out our meals to advancing our careers, every action we choose is derived from our drive to experience rewarding moments in life. Whether these moments are self-centered pleasures or the more generous of goals, whether they bring us immediate gratification or long-term success, they are still our perception of how important and valuable they are. And to some extent, these moments are the reason for our existence.

Our ability to achieve these precious moments seems to be correlated with intelligence; “Intelligence” is defined as the ability to acquire and apply knowledge and skills. People that are deemed by society as intelligent are not only capable of trading-off immediate satisfaction for long-term goals, but also a good, certain future for a possibly better, yet

1.1 What is deep reinforcement learning?

1.1.1 Deep reinforcement learning is a machine learning approach to artificial intelligence

1.1.2 Deep reinforcement learning is concerned with creating computer programs

1.1.3 Deep reinforcement learning agents can solve problems that require intelligence

1.1.4 Deep reinforcement learning agents improve their behavior through trial-and-error learning

1.1.5 Deep reinforcement learning agents learn from sequential feedback

1.1.6 Deep reinforcement learning agents learn from evaluative feedback

1.1.7 Deep reinforcement learning agents learn from sampled feedback

1.1.8 Deep reinforcement learning agents utilize powerful non-linear function approximation

1.2 The past, present, and future of deep reinforcement learning

1.2.1 Recent history of artificial intelligence and deep reinforcement learning

1.2.2 Artificial intelligence winters

1.2.3 The current state of artificial intelligence

1.2.4 Progress in deep reinforcement learning

1.2.5 Opportunities ahead

1.3 The suitability of deep reinforcement learning

1.3.1 What are the pros and cons?

1.3.2 Deep reinforcement learning’s strengths

1.3.3 Deep reinforcement learning’s weaknesses

1.4 Setting clear two-way expectations

1.4.1 What to expect from the book?

1.4.2 How to get the most out of this book?

1.4.3 Deep reinforcement learning development environment

1.5 Summary

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