# devsisters/DQN-tensorflow

Tensorflow implementation of Human-Level Control through Deep Reinforcement Learning

Repository: https://github.com/devsisters/DQN-tensorflow
Canonical: https://ross.abutalabs.com/products/dqn-tensorflow
Language: Python
License: MIT
License Family: permissive
Last push: 2019-04-18T18:36:45+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": 3762, "days_push": 2694, "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 2583, forks 756 (observed 2026-08-28T04:07:02.489209+00:00)

## What it is
A TensorFlow implementation of the Deep Q-Network (DQN) algorithm from the DeepMind paper 'Human-Level Control through Deep Reinforcement Learning'. It trains agents to play Atari games via OpenAI Gym, including experience replay and fixed Q-target networks.

## Use cases
- train a DQN agent to play Atari games like Breakout
- learn how deep Q-learning and experience replay are implemented
- reproduce results from the DeepMind DQN paper
- experiment with DQN variants like Double and Dueling DQN
- benchmark reinforcement learning training on a GPU

## When to choose
- you want a readable reference implementation of the original DQN paper
- you are studying or teaching deep reinforcement learning with classic TensorFlow
- you need a baseline for comparing DQN variants on Atari environments

## When to avoid
- you need modern, maintained RL libraries - it depends on TensorFlow 0.12 and Python 2.7-era tooling
- you want production RL training at scale
- you need PyTorch or current TensorFlow 2.x compatibility

## Facets
- artifact type: library
- maturity: abandoned
- function: machine-learning, reinforcement-learning, deep-learning
- domain: reinforcement-learning, machine-learning, deep-learning, gaming-tools
- platform: python
- tags: dqn, tensorflow, openai-gym, atari, q-learning, experience-replay, research-code, linux, gpu

## Member repositories
- devsisters/DQN-tensorflow (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:07:02.489209+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 2026-08-30T02:22:30.336672+00:00, confidence not recorded.
  - readme: https://github.com/devsisters/DQN-tensorflow (fetched 2026-08-28T04:07:02.489209+00:00, sha 8e5c2f40856b)
- Data as of 2026-08-30T08:39:29.467469+00:00.