What it does This repo is a curated tutorial track through reinforcement learning fundamentals. Each folder contains a Jupyter notebook walking through one technique, from tabular Q-learning up through Deep Deterministic Policy Gradients. The author also tacks on robotics integration via ROS/Gazebo and a benchmarking tutorial for comparing methods.

The interesting bit The README doesn’t sanitize its scars. Tutorial 8 (DOOM) is “unfinished,” Tutorial 10 (GPS) is “unfinished,” and Tutorial 15 (Vanilla Policy Gradient) “failed miserably.” That transparency is rare in educational repos, where dead ends usually get deleted instead of labeled.

Key highlights

• 9 completed tutorials spanning Q-learning, SARSA, DQN, and policy gradients
• OpenAI Gym integration starting from Tutorial 3
• One robotics crossover: RL with ROS and Gazebo
• Explicit “WIP” and “failed miserably” labels on incomplete work
• Heavy citation list including Watkins’ 1989 thesis and David Silver’s lectures

Caveats

• Several tutorials (8, 10, 11, 15) are unfinished or failed; the series is not comprehensive
• No candidate images provided, so visual learners get only notebooks and markdown
• Last major activity unclear; some OpenAI Gym links may predate the current API

Verdict Good for someone who wants to see RL algorithms implemented step-by-step and doesn’t mind hitting dead-ends alongside the author. Skip it if you need a polished, end-to-end course with guaranteed working examples for every technique listed.