Learning and Decision Making

Reinforcement learning revolves around the agent's capability to learn and make informed decisions. This chapter examines the intricacies of learning mechanisms and decision-making strategies that drive effective agent behavior.

You'll explore the fundamental concepts of policy and value functions, which form the foundation of decision-making processes. Understanding how these functions are derived and optimized is crucial for designing agents that can navigate complex environments adeptly. We'll then delve into the balance between exploration and exploitation, a key challenge in reinforcement learning. This involves determining when an agent should explore new actions to uncover potentially higher rewards versus exploiting known actions to maximize immediate gains.

As we progress, the chapter will introduce various learning algorithms that enable agents to update their knowledge and improve their decision-making abilities over time. These algorithms are designed to handle the uncertainties inherent in dynamic environments, ensuring that agents can adapt and thrive.

By the end of this chapter, you'll have gained a comprehensive understanding of how learning and decision-making intersect in reinforcement learning, equipping you with the tools to design robust agents capable of making strategic choices.