pyshka501 / Reinforcement-Learning-from-bandits-to-RLHF

What is Reinforcement-Learning-from-bandits-to-RLHF?

This repository packs a university-level course on reinforcement learning, kicking off with multi-armed bandits and scaling to RLHF for LLMs, all in Jupyter Notebooks using Python. It solves the gap between vague RL theory and real-world use by delivering math proofs, intuitive breakdowns, hands-on algorithm implementations, and research insights. Developers get ready-to-run notebooks for experimenting with TD learning, policy gradients, PPO, and more, without hunting scattered tutorials.

Why is it gaining traction?

Unlike repos cluttered with unmerged paths or out-of-bounds symlinks, this one cleanly combines bandits exploration strategies with modern RL pipelines, standing out for its progression from basics to RLHF. The hook is visual training demos in notebooks that demystify convergence and stability, helping users implement and tweak algos faster than piecing together Sutton & Barto with GitHub scraps. It's practical for bridging GitHub Actions workflows or VSCode setups into RL prototyping.

Who should use this?

RL newcomers in ML engineering roles building recommendation systems or game AIs, needing bandit baselines before deep RL. Researchers tuning PPO/GRPO for LLM alignment via RLHF pipelines. Data scientists at startups prototyping offline RL without full PhD prereqs like linear algebra and probability.

Verdict

Grab it if you're serious about RL foundations—strong README docs and clear structure outweigh the 11 stars and 1.0% credibility score. Still early with lectures rolling out, so fork and contribute notebooks for maturity.

(178 words)