What are some programming-oriented resources for reinforcement learning? 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.
Thank you in advance.
Arthur Juliani has some interesting Medium articles on reinforcement learning with TensorFlow backed up with code on GitHub.

Part 0 — Q-Learning Agents
Part 1 — Two-Armed Bandit
Part 1.5 — Contextual Bandits
Part 2 — Policy-Based Agents
Part 3 — Model-Based RL
Part 4 — Deep Q-Networks and Beyond
Part 5 — Visualizing an Agent’s Thoughts and Actions
Part 6 — Partial Observability and Deep Recurrent Q-Networks
Part 7 — Action-Selection Strategies for Exploration
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 &amp; 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 &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 Hands-On Reinforcement Learning with Python