# FareedKhan-dev/all-rl-algorithms

Implementation of all RL algorithms in a simpler way

Repository: https://github.com/FareedKhan-dev/all-rl-algorithms
Canonical: https://ross.abutalabs.com/products/all-rl-algorithms
Language: Jupyter Notebook
License: MIT
License Family: permissive
Topics: agent, llm, openai, python, reinforcement-learning, rl
Last push: 2025-08-29T04:52:41+00:00

## Health v2 (maintenance only)
Score: 37/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 39, release rhythm 35, longevity 37
- inputs: {"age_days": 521, "days_push": 369, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1918, forks 367 (observed 2026-08-28T04:05:54.016117+00:00)

## What it is
A collection of Jupyter Notebook implementations of 18 reinforcement learning algorithms written from scratch in Python with NumPy and PyTorch. It is an educational resource prioritizing readability over performance, including a cheat sheet for quick reference.

## Use cases
- learn reinforcement learning algorithms from scratch
- understand how RL algorithms work under the hood
- study RL fundamentals before applying them to LLMs
- find a quick reference cheat sheet for RL algorithms
- experiment with RL algorithm implementations in notebooks
- teach reinforcement learning concepts to beginners

## When to choose
- you want readable, educational implementations rather than optimized code
- you are a beginner learning RL step by step
- you prefer interactive Jupyter notebooks for experimentation
- you need a quick reference for RL formulas and pseudocode

## When to avoid
- you need a production-ready or performance-optimized RL library
- you want advanced features or a stable API for building applications
- you need distributed or large-scale RL training

## Facets
- artifact type: learning-resource
- maturity: active
- function: machine-learning, reinforcement-learning, developer-tools
- domain: reinforcement-learning, machine-learning, tutorials, education
- platform: python
- tags: reinforcement-learning, educational, jupyter-notebooks, from-scratch, cheat-sheet, pytorch, llm-alignment

## Member repositories
- FareedKhan-dev/all-rl-algorithms (main) score 37

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:05:54.016117+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T03:09:59.418609+00:00, confidence not recorded.
  - readme: https://github.com/FareedKhan-dev/all-rl-algorithms (fetched 2026-08-28T04:05:54.016117+00:00, sha ccedaca49b88)
- Data as of 2026-08-30T08:39:29.467469+00:00.