Reinforcement Learning Algorithms

This chapter delves into the algorithms that drive decision-making systems in dynamic environments, forming the core of reinforcement learning. Reinforcement learning algorithms enable agents to learn optimal strategies and adapt to changing conditions, allowing them to achieve desired outcomes across various contexts.

You'll explore the essential algorithms that empower agents to navigate complex environments. These algorithms enable agents to evaluate and improve their actions through trial and error, leveraging feedback from their surroundings. From foundational models like Q-learning and SARSA to more advanced approaches such as Deep Q-Networks (DQN) and Policy Gradient methods, each algorithm offers a unique perspective on solving reinforcement learning problems.

By the end of this chapter, you will gain insights into how these algorithms function and how they are applied to learn policies and value functions. Additionally, you will understand the intricacies of exploration versus exploitation, a critical concern in reinforcement learning, and how different strategies address this balance to optimize learning. With this knowledge, you'll be well-equipped to appreciate the nuances and applications of reinforcement learning algorithms in real-world scenarios.