# Paulescu/hands-on-rl

Free course that takes you from zero to Reinforcement Learning PRO 🦸🏻‍🦸🏽

Repository: https://github.com/Paulescu/hands-on-rl
Canonical: https://ross.abutalabs.com/products/paulescu-hands-on-rl
Homepage: http://realworldml.net/the-hands-on-reinforcement-learning-course
Language: Jupyter Notebook
License: MIT
License Family: permissive
Topics: reinforcement-learning, deep-reinforcement-learning
Last push: 2024-03-14T22:53:45+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 1772, "days_push": 902, "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 1572, forks 194 (observed 2026-08-28T04:05:05.497140+00:00)

## What it is
A free, hands-on Reinforcement Learning course taught through Jupyter Notebooks, taking learners from basic RL concepts to deep RL. It covers Q-learning, SARSA, parametric and deep Q-learning, and policy gradients through progressively harder problems like Taxi, Mountain Car, Cart Pole, and Lunar Lander.

## Use cases
- learn reinforcement learning from scratch
- understand q-learning with a worked example
- study sarsa on the mountain car problem
- learn deep q networks with cart pole
- understand policy gradients with lunar lander
- find a free hands-on RL course with python notebooks

## When to choose
- you are a beginner wanting a step-by-step practical RL course
- you prefer learning by coding in Jupyter notebooks
- you want intuition and tips explained alongside theory
- you do not yet know deep learning but want to reach deep RL

## When to avoid
- you need a production RL library or framework
- you want a formal university-style theoretical treatment
- you need coverage of the latest RL algorithms like PPO or RLHF
- you need actively updated content for recent library versions

## Facets
- artifact type: learning-resource
- maturity: maintenance
- function: reinforcement-learning, machine-learning, deep-learning
- domain: reinforcement-learning, machine-learning, tutorials, education
- platform: python, cross-platform
- tags: free-course, jupyter-notebooks, q-learning, sarsa, policy-gradients, deep-q-learning, gym-environments

## Member repositories
- Paulescu/hands-on-rl (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:05:05.497140+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 2026-08-30T03:58:05.500654+00:00, confidence not recorded.
  - readme: https://github.com/Paulescu/hands-on-rl (fetched 2026-08-28T04:05:05.497140+00:00, sha ad43ac530c95)
  - homepage: http://realworldml.net/the-hands-on-reinforcement-learning-course (fetched 2026-08-29T11:28:19.544284+00:00, sha 09fc114b8b21)
- Data as of 2026-08-30T08:39:29.467469+00:00.