Reinforcement Learning and Deep Reinforcement Learning

This chapter provides a concise explanation of the basic terminology and concepts in reinforcement learning. It will give you a good understanding of the basic reinforcement learning framework for developing artificial intelligent agents. This chapter will also introduce deep reinforcement learning and provide you with a flavor of the types of advanced problems the algorithms enable you to solve. You will find mathematical expressions and equations used in quite a few places in this chapter. Although there's enough theory behind reinforcement learning and deep reinforcement learning to fill a whole book, the key concepts that are useful for practical implementation are discussed in this chapter, so that when we actually implement the algorithms in Python to train our agents, you can clearly understand the logic behind them. It is perfectly alright if you are not able to grasp all of it in your first pass. You can always come back to this chapter and revise whenever you need a better understanding. 

We will cover the following topics in this chapter:

• What is reinforcement learning?
• The Markov Decision Process
• The reinforcement learning framework
• What is deep reinforcement learning?
• How do deep reinforcement learning agents work in practice?