Reinforcement Learning 1: Foundations

This is based on David Silver's course but targeting younger students within a shorter 50min format (missing the advanced derivations) + more examples and Colab code.
Slides: https://cwkx.github.io/data/teaching/...
Twitter:   / cwkx  
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Introduction
- definition
- examples
- comparison
A Brief History
- learning by trial and error
- optimal control and dynamic programming
- Monte Carlo tree search
- temporal difference algorithms
Key Concepts
- designing rewards
- action spaces
- observability
- information states
- policies
- value functions
- model
- taxonomy

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