# kuz/DeepMind-Atari-Deep-Q-Learner

The original code from the DeepMind article + my tweaks

Repository: https://github.com/kuz/DeepMind-Atari-Deep-Q-Learner
Canonical: https://ross.abutalabs.com/products/deepmind-atari-deep-q-learner
Homepage: http://www.nature.com/nature/journal/v518/n7540/full/nature14236.html
Language: Lua
License Family: other
Last push: 2018-01-23T03:06:50+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": 4206, "days_push": 3144, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases, no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1829, forks 528 (observed 2026-08-28T04:05:41.723972+00:00)

## What it is
The original DQN 3.0 source code published by DeepMind alongside the 2015 Nature paper 'Human-level control through deep reinforcement learning', written in Lua on Torch 7, with minor tweaks by the repository owner. It trains deep Q-networks to play Atari 2600 games using the Arcade Learning Environment (via Xitari/AleWrap), with CPU and CUDA GPU run scripts.

## Use cases
- reproduce the original DeepMind DQN Atari experiments
- study the reference implementation of deep Q-learning
- train a DQN agent to play Atari 2600 ROMs
- compare historical RL implementations against modern algorithms
- run deep reinforcement learning on GPU with CUDA

## When to choose
- you need the exact original code to reproduce the Nature 2015 DQN paper results
- you are studying the historical DQN 3.0 implementation for research or education
- you specifically work with Torch 7 / LuaJIT environments

## When to avoid
- you want to apply RL to a new problem - far more efficient modern algorithms exist
- you need an actively maintained or licensed codebase (no license, last updated 2018)
- you prefer current frameworks like Keras-RL, rllab, or PyTorch-based RL libraries
- you need Windows or macOS support - it requires Linux with apt-get

## Facets
- artifact type: library
- maturity: abandoned
- function: reinforcement-learning, machine-learning, deep-learning, gpu-computing
- domain: reinforcement-learning, machine-learning, deep-learning, gaming-tools
- platform: lua, cli
- tags: dqn, atari, arcade-learning-environment, torch7, deep-q-learning, research-code, historical, research, linux, gpu

## Member repositories
- kuz/DeepMind-Atari-Deep-Q-Learner (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:05:41.723972+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 2026-08-30T03:19:22.504019+00:00, confidence not recorded.
  - readme: https://github.com/kuz/DeepMind-Atari-Deep-Q-Learner (fetched 2026-08-28T04:05:41.723972+00:00, sha 20e262cad6fa)
  - homepage: http://www.nature.com/nature/journal/v518/n7540/full/nature14236.html (fetched 2026-08-29T10:58:31.562173+00:00, sha f0e45d9104df)
  - site_page: https://www.nature.com/openresearch/about-open-access/information-for-institutions (fetched 2026-08-29T10:58:31.572380+00:00, sha 9631b227fce6)
  - site_page: https://www.nature.com/npg_/company_info/index.html (fetched 2026-08-29T10:58:31.568869+00:00, sha 3ab76cca5d4a)
  - site_page: https://support.nature.com/en/support/home (fetched 2026-08-29T10:58:31.565310+00:00, sha c20191beeed1)
  - site_page: https://www.nature.com/npg_/press_room/press_releases.html (fetched 2026-08-29T10:58:31.570677+00:00, sha 705ec08d2ccd)
- Data as of 2026-08-30T08:39:29.467469+00:00.