Reinforcement learning (RL) is the third major section of machine learning after supervised and unsupervised learning. These techniques have gained a lot of traction in recent years in the application of artificial intelligence. In reinforcement learning, sequential decisions are to be made rather than one shot decision making, which makes it difficult to train the models in a few cases. In this chapter, we would be covering various techniques used in reinforcement learning with practical examples to support with. Though covering all topics are beyond the scope of this book, but we did cover the most important fundamentals here for a reader to create enough enthusiasm on this subject. Topics discussed in this chapter are:

• Markov decision process
• Bellman equations
• Dynamic programming
• Monte Carlo methods
• Temporal difference learning