📚 Module Slides

Welcome to the lecture slides for COM3240 Reinforcement Learning. Click any topic below to view or download the slides (PDF format).

Table of Contents

1. Introduction to RL.
2. Revisiting Probabilities: Dungeons, Dragons and other monsters.
3. Differentiation: The Concept of Optimisation, (Partial) Derivatives and (Partial) Differential Equations.
4. Reinforcement Learning through the lens of Optimisation; Immediate Rewards.
5. Markov Decision Processes and the Bellman Equations.
6. TD-Learning.
7. Artificial Neural Networks as Function Approximations: Delta rule.
8. Artificial Neural Networks as Function Approximations: Backprobagation.
9. Revision.

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