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

Deep-Reinforcement-Learning-Hands-On-Second-Edition, published by Packt

Repository: https://github.com/PacktPublishing/Deep-Reinforcement-Learning-Hands-On-Second-Edition
Canonical: https://ross.abutalabs.com/products/deep-reinforcement-learning-hands-on-second-edition
Language: Jupyter Notebook
License: MIT
License Family: permissive
Last push: 2024-04-04T18:46:48+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": 2618, "days_push": 881, "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 1288, forks 563 (observed 2026-08-28T04:04:15.184366+00:00)

## What it is
Companion code repository for the Packt book 'Deep Reinforcement Learning Hands-On, Second Edition', containing Jupyter Notebook examples of deep RL algorithms implemented in PyTorch. It is maintained to keep dependency versions compatible with newer PyTorch releases.

## Use cases
- learn deep reinforcement learning from scratch
- example code for DQN and policy gradient methods
- pytorch reinforcement learning tutorials
- study RL algorithms with runnable notebooks
- hands-on practice with gym environments and RL

## When to choose
- you are reading the book and want its code
- you want runnable PyTorch examples of classic deep RL algorithms
- you learn best by experimenting with notebooks

## When to avoid
- you need a production RL library or framework
- you need support for recent Python or PyTorch versions beyond what's tested
- you want a maintained general-purpose RL toolkit

## Facets
- artifact type: learning-resource
- maturity: maintenance
- function: reinforcement-learning, machine-learning, deep-learning
- domain: reinforcement-learning, deep-learning, machine-learning, tutorials
- platform: python, cross-platform
- tags: jupyter-notebook, pytorch, book-code, packt, rl-examples, gpu

## Member repositories
- PacktPublishing/Deep-Reinforcement-Learning-Hands-On-Second-Edition (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:04:15.184366+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 2026-08-30T04:55:47.622690+00:00, confidence not recorded.
  - readme: https://github.com/PacktPublishing/Deep-Reinforcement-Learning-Hands-On-Second-Edition (fetched 2026-08-28T04:04:15.184366+00:00, sha e40b32c3402e)
- Data as of 2026-08-30T08:39:29.467469+00:00.