Foundations of Reinforcement Learning

Reinforcement learning is a pivotal area within machine learning that concentrates on how agents acquire the ability to make decisions through interactions with their surroundings. In this chapter, we will explore the fundamental elements necessary to comprehend how reinforcement learning operates and its significance in developing intelligent systems.

We will commence by examining the core concepts that form the foundation of reinforcement learning, such as agents, environments, and the notion of reward. You will learn how these elements interact to establish the basis of decision-making processes that aim to maximize cumulative rewards over time. Key topics will include the agent-environment interface and the formal definition of a Markov Decision Process (MDP), which provides the mathematical framework for modeling decision-making problems.

Moreover, we'll introduce essential components like policies, which define the agent's behavior, and value functions, which evaluate the desirability of states or actions. You'll see how these elements work together to guide an agent's learning process. We will also touch upon the exploration-exploitation trade-off, a fundamental challenge in reinforcement learning, and discuss strategies for balancing the need to explore new actions with exploiting known rewards.

By the end of this chapter, you will have a solid grasp of the foundational principles of reinforcement learning, setting the stage for deeper explorations into algorithms and applications in subsequent chapters.