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I have been reading: Reinforcement Learning: An Introduction by Sutton and Barto. 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.

nbro
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Arpit-Gole
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2 Answers2

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Arthur Juliani has some interesting Medium articles on reinforcement learning with TensorFlow backed up with code on GitHub.

  1. Part 0 — Q-Learning Agents
  2. Part 1 — Two-Armed Bandit
  3. Part 1.5 — Contextual Bandits
  4. Part 2 — Policy-Based Agents
  5. Part 3 — Model-Based RL
  6. Part 4 — Deep Q-Networks and Beyond
  7. Part 5 — Visualizing an Agent’s Thoughts and Actions
  8. Part 6 — Partial Observability and Deep Recurrent Q-Networks
  9. Part 7 — Action-Selection Strategies for Exploration
  10. Part 8 — Asynchronous Actor-Critic Agents (A3C)

As nbro 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

Brian O'Donnell
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I suggest you to have a look at this 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. This repo contains many programming and reinforcement learning examples with PyTorch framework.

dasmehdix
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