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deepmind-x-ucl-rl

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lecture slides for Deepmind x UCL 2021 reinforcement learning course available in YouTbue

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최종 버전 다운로드 (.zip)

- All Lectures.pdf
- Lecture 1 - introduction.pdf
- Lecture 10- Approximate Dynamic Programming.pdf
- Lecture 11- Off-policy and multi-step.pdf
- Lecture 12- Deep RL 1 .pdf
- Lecture 13 - Deep RL 2.pdf
- Lecture 2- Exploration and control_slides.pdf
- Lecture 3 - MDPs and Dynamic Programming.pdf
- Lecture 4 - Theoretical Fundamentals of DP Algorithms.pdf
- Lecture 5 - ModelFreePrediction.pdf
- Lecture 6 - Model-free control.pdf
- Lecture 7- Function approximation in reinforcement learning .pdf
- Lecture 8 - Model Based Reinforcement Learning.pdf
- Lecture 9- Policy gradients and actor critics.pdf
README.md

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# Deepmind x UCL | Reinforcement Learning Course | 2021
this repo contains all of the lecture slides for Deepmind x UCL RL course taught in 2021.

- presenters: Hado van Hasselt, Diana Borsa, Matteo Hessel

- [youtube playlist](https://dpmd.ai/DeepMindxUCL21)

- all slides available as a single pdf file: [All Lectures](<./slides/All Lectures.pdf>)

## Slides
1. [Lecture 1 - introduction](<./slides/Lecture 1 - introduction.pdf>)
2. [Lecture 2- Exploration and control_slides](<./slides/Lecture 2- Exploration and control_slides.pdf>)
3. [Lecture 3 - MDPs and Dynamic Programming](<./slides/Lecture 3 - MDPs and Dynamic Programming.pdf>)
4. [Lecture 4 - Theoretical Fundamentals of DP Algorithms](<./slides/Lecture 4 - Theoretical Fundamentals of DP Algorithms.pdf>)
5. [Lecture 5 - ModelFreePrediction](<./slides/Lecture 5 - ModelFreePrediction.pdf>)
6. [Lecture 6 - Model-free control](<./slides/Lecture 6 - Model-free control.pdf>)
7. [Lecture 7- Function approximation in reinforcement learning ](<./slides/Lecture 7- Function approximation in reinforcement learning .pdf>)
8. [Lecture 8 - Model Based Reinforcement Learning](<./slides/Lecture 8 - Model Based Reinforcement Learning.pdf>)
9. [Lecture 9- Policy gradients and actor critics](<./slides/Lecture 9- Policy gradients and actor critics.pdf>)
10. [Lecture 10- Approximate Dynamic Programming](<./slides/Lecture 10- Approximate Dynamic Programming.pdf>)
11. [Lecture 11- Off-policy and multi-step](<./slides/Lecture 11- Off-policy and multi-step.pdf>)
12. [Lecture 12- Deep RL 1 ](<./slides/Lecture 12- Deep RL 1 .pdf>)
13. [Lecture 13 - Deep RL 2](<./slides/Lecture 13 - Deep RL 2.pdf>)