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Reinforcement Learning

These notes are from David Silver's Reinforcement Learning course.

I've tried to write down most of the explanations he goes through and expanded on certain sections I personally felt needed simplification. I'll be linking them below as I progress through the lectures.

There might be notational inconsistencies, in which case the source material supersedes everything. If there's a conceptual flaw in my writing, feel free to reach out.

• Lecture 1: Introduction to Reinforcement Learning
• Lecture 2: Markov Decision Processes
• Lecture 3: Planning by Dynamic Programming
• Lecture 4: Model-Free Prediction
• Lecture 5: Model-Free Control
• Lecture 6: Value Function Approximation
• Lecture 7: Policy Gradient Methods
• Lecture 8: Integrating Learning and Planning
• Lecture 9: Exploration and Exploitation
• Lecture 10: Case Study: RL in Classic Games