Introduction to Reinforcement Learning: Foundations and Algorithms

Reinforcement learning is the driving force behind modern autonomous systems, game-playing agents, and adaptive decision-making algorithms. Understanding how agents learn from interaction is essential for anyone looking to enter the field of advanced artificial intelligence. This text-only course guides you from foundational probability and decision theory to implementing classic and modern reinforcement learning algorithms. You will build a solid theoretical understanding and learn how to translate these concepts into clean, functional code. What you'll learn: - Understand the mathematical foundations of Markov Decision Processes (MDPs) and dynamic programming. - Implement classic tabular methods including Monte Carlo and Temporal Difference learning. - Explore value-based and policy-based methods for complex decision-making environments. - Apply deep reinforcement learning concepts using deep Q-networks (DQN) and modern neural network architectures. - Practice building and training agents using standard simulation environments and modern Python libraries. - Configure and tune hyperparameters to stabilize learning and improve agent performance. The course begins with essential terminology, probability basics, and the agent-environment interface before moving systematically into value functions, policy iteration, and deep learning integrations. Each concept is reinforced with step-by-step written walkthroughs and clear code snippets. This course is designed for beginners in machine learning, software developers, and students who want a structured, text-based introduction to reinforcement learning without needing prior experience in the subject. Start building intelligent, adaptive agents today.