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2023/SP23/deep-rl/README.md

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---
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title: "Deep Reinforcement Learning"
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description: "Join us for an immersive workshop on Deep Reinforcement Learning (DRL), a cutting-edge approach for solving complex decision-making problems that is revolutionizing artificial intelligence. This workshop and repository will provide participants with a comprehensive understanding of the fundamental concepts, techniques, and applications of Deep RL."
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---
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<!--
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The above is REQUIRED YAML Frontmatter so the README can be rendered
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in the ACM Wiki. DO NOT move the frontmatter from its location.
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It is also ESSENTIAL to fill out; make it good!
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<!--
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If you have any questions about this template, feel free to ask
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your Director for help!
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<!--
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SECTION: Header
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---------
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Request new headers from you Director to fit your workshop!
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![Alt Text (This should be your workshop title written so it's screenreader-compatible)](./figures/Default_Header_Light.png#gh-light-mode-only)
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![Alt Text (This should be your workshop title written so it's screenreader-compatible)](./figures/Default_Header_Dark.png#gh-dark-mode-only)
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The official ACM AI "Deep Reinforcement Learning" repository.
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**Description**: Join us for an immersive workshop on **Deep Reinforcement Learning** (DRL), a cutting-edge approach for solving complex decision-making problems that is revolutionizing artificial intelligence. This workshop and repository will provide participants with a comprehensive understanding of the fundamental concepts, techniques, and applications of Deep RL.
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We will cover the following set of topics:
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1. Reinforcement Learning Framework
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- Types of Tasks
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- Exploration and Exploitation
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- 2 Main Approaches for solving problems with RL
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2. From Q-Learning to Deep Q-Learning
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- Q-Learning Algorithm
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- Q-Learning Example
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- Deep Q-Network (DQN)
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- Deep Q Algorithm
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- Deep Q-Network Example
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3. Beyond Deep Q-Learning
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- Overview of popular Deep RL algorithms
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- Successful (and un-successful) Applications of Deep RL
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- Open problems in Deep RL Research
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<!--
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Mandatory Sections:
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- File Directory Structure
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- Workshop Recording
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- if you recorded your workshop, please make it available here
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- Getting Started
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- Give an interesting description of your workshop!
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- E.g. you can use the marketing descriptiong (w/o the emojis
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and make the nouns general ('you' becomes 'the reader'))
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- Resources
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- Images, papers, etc
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- Author Info
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- About yourself! This is so you get credit for your work!
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Other Possible Sections:
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- Datasets
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- Separate from 'Resources' to distinguish between inspiration and
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things like datasets used in a notebook
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- Anything else you'd like, but try not to be redundant!
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-->
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<!--
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SECTION: Workshop Video
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---------
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Most, if not all, workshops should have recordings. Once the recording
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is posted to the ACMUCSD YT channel (https://www.youtube.com/channel/UCyjPATFqc3FwOiuqJ2UG1Eg),
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replace the text with an <img> element.
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# 1. Workshop Video
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*Will be added as soon as uploaded to YouTube*
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<div align="center">
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<a href="YT Video Link">
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<img
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src="YT Max Res Thumbnail Link"
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alt="Screen reader-compatible alt text"
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width="500px"
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/>
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</a>
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</div>
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<!--
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SECTION: File Directory Structure
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Write out your File Directory Structure below (make sure it's up-to-date)
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# 2. File Directory Structure
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```bash
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deep-rl
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| -- figures
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| -- Default_Header_Dark.png
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| -- Default_Header_Light.png
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| -- src
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| -- README.md
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```
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<!--
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SECTION: Getting Started
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Brief description of your workshop here
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# 3. Getting Started
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You can write something up or use the marketing description.
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*To Be Updated!*
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<!--
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SECTION: Resources
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Make sure to cite everything you use, whether directly or for inspiration!
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# 4. Resources
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List all your resources below; DO NOT skimp on this part!
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- [The RL Book](http://incompleteideas.net/book/the-book.html)
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- [Deep RL Course @ UCB](https://rail.eecs.berkeley.edu/deeprlcourse/)
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<!--
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SECTION: Author Info
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Make sure to give yourself credit for your work by listing yourself and
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your partners below! Add your LinkedIn and GitHub!
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-->
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# 5. Author Info
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- Jonathan Zamora: [LinkedIn](https://www.linkedin.com/in/jonzamora18) | [GitHub](https://github.com/jonzamora)
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