1 Introduction to deep reinforcement learning
- You will learn what deep reinforcement learning is and how it is different from other machine learning approaches.
- You will learn about the recent progress in deep reinforcement learning and what it can do for a variety of problems.
- You will 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’re still our perception of how important and valuable they are. And to some extent, these moments are the reason for our existence.
What is deep reinforcement learning?
Deep reinforcement learning is a machine learning approach to artificial intelligence
Deep reinforcement learning is concerned with creating computer programs
Deep reinforcement learning agents can solve problems that require intelligence
Deep reinforcement learning agents improve their behavior through trial-and-error learning
Deep reinforcement learning agents learn from sequential feedback
Deep reinforcement learning agents learn from evaluative feedback
Deep reinforcement learning agents learn from sampled feedback
Deep reinforcement learning agents use powerful non-linear function approximation
The past, present, and future of deep reinforcement learning
Recent history of artificial intelligence and deep reinforcement learning
Artificial intelligence winters
The current state of artificial intelligence
Progress in deep reinforcement learning
Opportunities ahead
The suitability of deep reinforcement learning
What are the pros and cons?
Deep reinforcement learning’s strengths
Deep reinforcement learning’s weaknesses
Setting clear two-way expectations
What to expect from the book?
How to get the most out of this book
Deep reinforcement learning development environment
Summary