# PacktPublishing/Deep-Reinforcement-Learning-Hands-On

Hands-on Deep Reinforcement Learning, published by Packt

Repository: https://github.com/PacktPublishing/Deep-Reinforcement-Learning-Hands-On
Canonical: https://ross.abutalabs.com/products/deep-reinforcement-learning-hands-on
Language: Python
License: MIT
License Family: permissive
Last push: 2026-03-02T14:35:56+00:00

## Health v2 (maintenance only)
Score: 64/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 70, release rhythm 35, longevity 100
- inputs: {"age_days": 3092, "days_push": 184, "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 3102, forks 1321 (observed 2026-08-28T04:07:43.633580+00:00)

## What it is
Companion code repository for the Packt book 'Deep Reinforcement Learning Hands-On', containing Python examples built with PyTorch and OpenAI Gym. It is maintained by the book author to keep examples compatible with recent library versions.

## Use cases
- learn deep reinforcement learning from scratch
- find DQN example code in PyTorch
- study policy gradient implementations
- get working RL examples updated for latest PyTorch
- practice reinforcement learning with OpenAI Gym
- follow along with a hands-on RL book

## When to choose
- you are reading the book and want its runnable code
- you want practical, chapter-organized RL examples in PyTorch
- you learn best from working sample code

## When to avoid
- you need a production-ready RL library or framework
- you want a maintained algorithm library rather than educational samples
- you need support for discontinued environments like OpenAI Universe

## Facets
- artifact type: learning-resource
- maturity: active
- function: machine-learning, reinforcement-learning, developer-tools
- domain: reinforcement-learning, machine-learning, deep-learning, tutorials
- platform: python, cross-platform
- tags: pytorch, openai-gym, book-code, deep-reinforcement-learning, code-examples

## Member repositories
- PacktPublishing/Deep-Reinforcement-Learning-Hands-On (main) score 64

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:07:43.633580+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 2026-08-30T07:26:58.750192+00:00, confidence not recorded.
  - readme: https://github.com/PacktPublishing/Deep-Reinforcement-Learning-Hands-On (fetched 2026-08-28T04:07:43.633580+00:00, sha f0e0ce3af876)
- Data as of 2026-08-30T08:39:29.467469+00:00.