Sample-Based Learning Methods for Reinforcement Learning

Building intelligent systems often requires learning from experience when a perfect model of the world is unavailable. This course introduces you to the core algorithms that enable agents to improve their decision-making through direct interaction and feedback. You will transition from understanding basic agent-environment loops to implementing sophisticated strategies that solve complex tasks without prior knowledge of environmental dynamics. By the end of this course, you will be able to design systems that learn from their own successes and failures. What you'll learn: - Understand the foundational concepts of states, actions, and rewards in learning systems. - Implement Monte Carlo methods to evaluate and improve policies based on experience. - Master Temporal Difference learning, including the mechanics of Q-learning and SARSA. - Apply exploration-exploitation strategies to balance discovering new paths with maximizing rewards. - Practice value function estimation to predict long-term outcomes in dynamic settings. - Explore modern function approximation basics to help learning methods scale to larger problems. This course begins with essential terminology and the mathematical foundations of reinforcement learning before progressing to practical algorithmic applications through written explanations and code examples. It is designed for beginners who want a solid conceptual and practical grounding in how machines learn from experience. Begin your journey into autonomous learning and start building agents that adapt to the world around them.