Answers.Body: <p>Arthur Juliani has some interesting Medium articles on reinforcement learning with TensorFlow backed up with code on <a href="https://github.com/awjuliani/DeepRL-Agents" rel="nofollow noreferrer">GitHub</a>.</p>
  <ol>
  <li><a href="https://medium.com/emergent-future/simple-reinforcement-learning-with-tensorflow-part-0-q-learning-with-tables-and-neural-networks-d195264329d0" rel="nofollow noreferrer">Part 0 — Q-Learning Agents</a></li>
  <li><a href="https://medium.com/@awjuliani/super-simple-reinforcement-learning-tutorial-part-1-fd544fab149" rel="nofollow noreferrer">Part 1 — Two-Armed Bandit</a></li>
  <li><a href="https://medium.com/@awjuliani/simple-reinforcement-learning-with-tensorflow-part-1-5-contextual-bandits-bff01d1aad9c" rel="nofollow noreferrer">Part 1.5 — Contextual Bandits</a></li>
  <li><a href="https://medium.com/@awjuliani/super-simple-reinforcement-learning-tutorial-part-2-ded33892c724" rel="nofollow noreferrer">Part 2 — Policy-Based Agents</a></li>
  <li><a href="https://medium.com/@awjuliani/simple-reinforcement-learning-with-tensorflow-part-3-model-based-rl-9a6fe0cce99" rel="nofollow noreferrer">Part 3 — Model-Based RL</a></li>
  <li><a href="https://medium.com/@awjuliani/simple-reinforcement-learning-with-tensorflow-part-4-deep-q-networks-and-beyond-8438a3e2b8df" rel="nofollow noreferrer">Part 4 — Deep Q-Networks and Beyond</a></li>
  <li><a href="https://medium.com/@awjuliani/simple-reinforcement-learning-with-tensorflow-part-5-visualizing-an-agents-thoughts-and-actions-4f27b134bb2a" rel="nofollow noreferrer">Part 5 — Visualizing an Agent’s Thoughts and Actions</a></li>
  <li><a href="https://medium.com/@awjuliani/simple-reinforcement-learning-with-tensorflow-part-6-partial-observability-and-deep-recurrent-q-68463e9aeefc" rel="nofollow noreferrer">Part 6 — Partial Observability and Deep Recurrent Q-Networks</a></li>
  <li><a href="https://medium.com/@awjuliani/simple-reinforcement-learning-with-tensorflow-part-7-action-selection-strategies-for-exploration-d3a97b7cceaf" rel="nofollow noreferrer">Part 7 — Action-Selection Strategies for Exploration</a></li>
  <li><a href="https://medium.com/@awjuliani/simple-reinforcement-learning-with-tensorflow-part-8-asynchronous-actor-critic-agents-a3c-c88f72a5e9f2" rel="nofollow noreferrer">Part 8 — Asynchronous Actor-Critic Agents (A3C)</a></li>
  </ol>
  <p>As <a href="https://ai.stackexchange.com/users/2444/nbro">nbro</a> pointed out Denny Britz has a good repository: <a href="https://github.com/dennybritz/reinforcement-learning" rel="nofollow noreferrer">https://github.com/dennybritz/reinforcement-learning</a></p>
  <p>As you have seen with Sutton &amp; Barto's book the code is mostly in Lisp.  Shangtong Zhang has replicated the code in Python: <a href="https://github.com/ShangtongZhang/reinforcement-learning-an-introduction" rel="nofollow noreferrer">https://github.com/ShangtongZhang/reinforcement-learning-an-introduction</a></p>
  <p>Sudharsan and Ravichandiran wrote a book &quot;Hands-On Reinforcement Leraning with Python&quot; which uses OpenAI Gym and TensorFlow.  You can find more information on the book along with their code repository at <a href="https://github.com/sudharsan13296/Hands-On-Reinforcement-Learning-With-Python" rel="nofollow noreferrer">Hands-On Reinforcement Learning with Python</a></p>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2020-09-05T21:42:15.307Z
Answers.Text: You may also want to include this repo https://github.com/dennybritz/reinforcement-learning in your answer. It contains notebooks with programming exercises where you need to implement RL algorithms.
Answers.UserDisplayName: nbro
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2020-09-05T19:33:29.860Z
Answers.LastActivityDate: 2020-09-06T00:24:47.317Z
Answers.LastEditDate: 2020-09-06T00:24:47.317Z
Answers.UserDisplayName: Brian O'Donnell
Answers.Body: <p>I suggest you to have a look at <a href="https://github.com/aikorea/awesome-rl" rel="nofollow noreferrer">this</a> repo. It contains state-of-the art algorithms, papers, frameworks, courses and some implementations. You can also check &quot;Deep Reinforcement Learning Hands On&quot; book examples written by Max Lapan <a href="https://github.com/PacktPublishing/Deep-Reinforcement-Learning-Hands-On" rel="nofollow noreferrer">here</a>. This repo contains many programming and reinforcement learning examples with PyTorch framework.</p>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2020-09-07T11:26:08.583Z
Answers.Text: Thanks for addressing my concern, but how does this directly answers the question? The OP was looking for resources with programming exercises, if I understood correctly. Maybe you can provide a more specific answer.
Answers.UserDisplayName: nbro
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2020-09-08T08:51:10.750Z
Answers.Text: Double edited :)
Answers.UserDisplayName: dasmehdix
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2020-09-07T10:43:48.980Z
Answers.LastActivityDate: 2020-09-08T08:50:34.753Z
Answers.LastEditDate: 2020-09-08T08:50:34.753Z
Answers.UserDisplayName: dasmehdix
Body: <p>I have been reading: <a href="http://incompleteideas.net/book/the-book-2nd.html" rel="nofollow noreferrer">Reinforcement Learning: An Introduction by Sutton and Barto</a>. I admit it's a good read for learning RL whereas it's more theoretical with detailed algorithms.</p>
  <p>Now, <strong>I want something more programming oriented resource(s)</strong> maybe a course, book, etc. I have been exploring Kaggle, Open-source RL projects.</p>
  <p>I need this to learn and grasp a deeper understanding of RL from the perspective of a developer i.e optimized way of writing code, explanation about using the latest RL libraries, cloud services, etc.</p>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2020-09-05T16:58:20.587Z
Comments.Text: Please, have a look at [this](https://ai.stackexchange.com/q/18798/2444), [this](https://ai.stackexchange.com/q/22010/2444) and [this question](https://ai.stackexchange.com/q/6997/2444). If any of those answers answer your question, let me know.
Comments.UserDisplayName: nbro
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2020-09-06T02:40:17.727Z
Comments.Text: Thank you @nbro, I got what I needed. I see you are leading the AI SE community impressively. Keep up the good work.
Comments.UserDisplayName: Arpit-Gole
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2020-09-07T13:46:35.173Z
Comments.Text: If you found what you were looking for and it's in the answers below, please, click on the "check" mark to accept the answer. If it's somewhere else (e.g. in the answer to another question), please, let us know.
Comments.UserDisplayName: nbro
Tags: <reinforcement-learning><python><resource-request>
Title: What are some programming-oriented resources for reinforcement learning?
UserDisplayName: Arpit-Gole
ThreadText: # What are some programming-oriented resources for reinforcement learning?
  
  (Asked by: username_0 on 2020-09-05)
  
  I have been reading: [Reinforcement Learning: An Introduction by Sutton and Barto](http://incompleteideas.net/book/the-book-2nd.html). I admit it's a good read for learning RL whereas it's more theoretical with detailed algorithms.
  
  Now, **I want something more programming oriented resource(s)** maybe a course, book, etc. I have been exploring Kaggle, Open-source RL projects.
  
  I need this to learn and grasp a deeper understanding of RL from the perspective of a developer i.e optimized way of writing code, explanation about using the latest RL libraries, cloud services, etc.
  
  **Tags:** reinforcement-learning, python, resource-request
  
  > **Comments:**
  > **username_1**: Please, have a look at [this](https://ai.stackexchange.com/q/18798/2444), [this](https://ai.stackexchange.com/q/22010/2444) and [this question](https://ai.stackexchange.com/q/6997/2444). If any of those answers answer your question, let me know.  
  > **username_0**: Thank you @username_1, I got what I needed. I see you are leading the AI SE community impressively. Keep up the good work.  
  > **username_1**: If you found what you were looking for and it's in the answers below, please, click on the "check" mark to accept the answer. If it's somewhere else (e.g. in the answer to another question), please, let us know.  
  
  ---
  
  ## 2 answers
  
  ### Answer by username_2 on 2020-09-05. Score: 2
  
  Arthur Juliani has some interesting Medium articles on reinforcement learning with TensorFlow backed up with code on [GitHub](https://github.com/awjuliani/DeepRL-Agents).
  
  1. [Part 0 — Q-Learning Agents](https://medium.com/emergent-future/simple-reinforcement-learning-with-tensorflow-part-0-q-learning-with-tables-and-neural-networks-d195264329d0)
  2. [Part 1 — Two-Armed Bandit](https://medium.com/@awjuliani/super-simple-reinforcement-learning-tutorial-part-1-fd544fab149)
  3. [Part 1.5 — Contextual Bandits](https://medium.com/@awjuliani/simple-reinforcement-learning-with-tensorflow-part-1-5-contextual-bandits-bff01d1aad9c)
  4. [Part 2 — Policy-Based Agents](https://medium.com/@awjuliani/super-simple-reinforcement-learning-tutorial-part-2-ded33892c724)
  5. [Part 3 — Model-Based RL](https://medium.com/@awjuliani/simple-reinforcement-learning-with-tensorflow-part-3-model-based-rl-9a6fe0cce99)
  6. [Part 4 — Deep Q-Networks and Beyond](https://medium.com/@awjuliani/simple-reinforcement-learning-with-tensorflow-part-4-deep-q-networks-and-beyond-8438a3e2b8df)
  7. [Part 5 — Visualizing an Agent’s Thoughts and Actions](https://medium.com/@awjuliani/simple-reinforcement-learning-with-tensorflow-part-5-visualizing-an-agents-thoughts-and-actions-4f27b134bb2a)
  8. [Part 6 — Partial Observability and Deep Recurrent Q-Networks](https://medium.com/@awjuliani/simple-reinforcement-learning-with-tensorflow-part-6-partial-observability-and-deep-recurrent-q-68463e9aeefc)
  9. [Part 7 — Action-Selection Strategies for Exploration](https://medium.com/@awjuliani/simple-reinforcement-learning-with-tensorflow-part-7-action-selection-strategies-for-exploration-d3a97b7cceaf)
  10. [Part 8 — Asynchronous Actor-Critic Agents (A3C)](https://medium.com/@awjuliani/simple-reinforcement-learning-with-tensorflow-part-8-asynchronous-actor-critic-agents-a3c-c88f72a5e9f2)
  
  As [username_1](https://ai.stackexchange.com/users/2444/username_1) pointed out Denny Britz has a good repository: <https://github.com/dennybritz/reinforcement-learning>
  
  As you have seen with Sutton & Barto's book the code is mostly in Lisp. Shangtong Zhang has replicated the code in Python: <https://github.com/ShangtongZhang/reinforcement-learning-an-introduction>
  
  Sudharsan and Ravichandiran wrote a book "Hands-On Reinforcement Leraning with Python" which uses OpenAI Gym and TensorFlow. You can find more information on the book along with their code repository at [Hands-On Reinforcement Learning with Python](https://github.com/sudharsan13296/Hands-On-Reinforcement-Learning-With-Python)
  
  > **Comments:**
  > **username_1**: You may also want to include this repo https://github.com/dennybritz/reinforcement-learning in your answer. It contains notebooks with programming exercises where you need to implement RL algorithms.  
  
  ---
  
  ### Answer by username_3 on 2020-09-07. Score: 1
  
  I suggest you to have a look at [this](https://github.com/aikorea/awesome-rl) repo. It contains state-of-the art algorithms, papers, frameworks, courses and some implementations. You can also check "Deep Reinforcement Learning Hands On" book examples written by Max Lapan [here](https://github.com/PacktPublishing/Deep-Reinforcement-Learning-Hands-On). This repo contains many programming and reinforcement learning examples with PyTorch framework.
  
  > **Comments:**
  > **username_1**: Thanks for addressing my concern, but how does this directly answers the question? The OP was looking for resources with programming exercises, if I understood correctly. Maybe you can provide a more specific answer.  
  > **username_3**: Double edited :)  
  
  ---
  
  