Reinforcement Learning Algorithms with Python

Model-Based RL

Reinforcement learning algorithms are divided into two classes—model-free methods and model-based methods. These two classes differ by the assumption made about the model of the environment. Model-free algorithms learn a policy from mere interactions with the environment without knowing anything about it, whereas model-based algorithms already have a deep understanding of the environment and use this knowledge to take the next actions according to the dynamics of the model.

In this chapter, we'll give you a comprehensive overview of model-based approaches, highlighting their advantages and disadvantages vis-à-vis model-free approaches, and the differences that arise when the model is known or has to be learned. This latter ...