# Curt-Park/rainbow-is-all-you-need

Rainbow is all you need! A step-by-step tutorial from DQN to Rainbow

Repository: https://github.com/Curt-Park/rainbow-is-all-you-need
Canonical: https://ross.abutalabs.com/products/rainbow-is-all-you-need
Language: Python
License: MIT
License Family: permissive
Topics: pytorch, reinforcement-learning, dqn, rainbow, gym-environment, colab-notebook, nbviewer
Last push: 2026-06-20T12:14:55+00:00

## Health v2 (maintenance only)
Score: 72/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 88, release rhythm 35, longevity 100
- inputs: {"age_days": 2641, "days_push": 74, "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 2031, forks 353 (observed 2026-08-28T04:06:07.283264+00:00)

## What it is
A step-by-step tutorial of interactive Python notebooks teaching value-based deep reinforcement learning, progressing from DQN through its key improvements (Double DQN, Prioritized Experience Replay, Dueling Networks, NoisyNet, Categorical DQN, N-step learning) to Rainbow and Rainbow IQN. Each chapter pairs theoretical background with object-oriented PyTorch implementations that run in the cloud via molab or locally with marimo.

## Use cases
- learn deep reinforcement learning from scratch
- understand how DQN works with code
- implement rainbow RL in pytorch
- tutorial on prioritized experience replay
- study dueling networks and noisy nets
- understand distributional reinforcement learning
- run RL notebook examples in the browser
- step-by-step guide from DQN to Rainbow

## When to choose
- You want to learn value-based deep RL methods with annotated, runnable PyTorch code
- You prefer theory paired with clean object-oriented implementations in interactive notebooks
- You want zero-setup cloud execution of RL examples via molab

## When to avoid
- You need a production-ready RL library or framework rather than educational notebooks
- You are looking for policy-gradient methods like PPO or A2C (see PG is All You Need instead)
- You need scalable or distributed RL training infrastructure

## Facets
- artifact type: learning-resource
- maturity: stable
- function: reinforcement-learning, deep-learning, machine-learning
- domain: reinforcement-learning, deep-learning, education, tutorials
- platform: python
- tags: pytorch, dqn, rainbow, deep-q-network, q-learning, prioritized-experience-replay, dueling-network, noisy-net, distributional-rl, n-step-learning, iqn, marimo, notebook, gymnasium, tutorial, gpu

## Member repositories
- Curt-Park/rainbow-is-all-you-need (main) score 72

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:06:07.283264+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 2026-08-30T02:59:30.923223+00:00, confidence not recorded.
  - readme: https://github.com/Curt-Park/rainbow-is-all-you-need (fetched 2026-08-28T04:06:07.283264+00:00, sha bf1a90ff5b3c)
- Data as of 2026-08-30T08:39:29.467469+00:00.