# AppliedDataSciencePartners/DeepReinforcementLearning

A replica of the AlphaZero methodology for deep reinforcement learning in Python

Repository: https://github.com/AppliedDataSciencePartners/DeepReinforcementLearning
Canonical: https://ross.abutalabs.com/products/deepreinforcementlearning
Language: Jupyter Notebook
License: GPL-3.0
License Family: copyleft
Last push: 2022-11-21T22:18:26+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": 3142, "days_push": 1381, "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 2033, forks 749 (observed 2026-08-28T04:06:07.814301+00:00)

## What it is
A Python implementation replicating the AlphaZero deep reinforcement learning methodology, built with Keras and Jupyter notebooks. It serves primarily as an educational reference for understanding self-play, Monte Carlo tree search, and neural network-based game AI.

## Use cases
- learn how alphazero works
- implement self-play reinforcement learning
- build an ai that plays board games
- understand monte carlo tree search with neural networks
- study deep reinforcement learning code
- replicate alphazero in python

## When to choose
- you want a readable, minimal AlphaZero reference implementation
- you're learning deep RL and want to follow a blog walkthrough
- you need a starting point for game-playing AI experiments

## When to avoid
- you need a production-grade or maintained RL framework
- you want support for modern RL algorithms beyond AlphaZero
- you need GPU-optimized large-scale training

## Facets
- artifact type: learning-resource
- maturity: maintenance
- function: reinforcement-learning, deep-learning, machine-learning
- domain: reinforcement-learning, deep-learning, machine-learning, tutorials
- platform: python
- tags: alphazero, self-play, mcts, keras, game-ai, jupyter-notebook, educational, algorithms

## Member repositories
- AppliedDataSciencePartners/DeepReinforcementLearning (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:06:07.814301+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 2026-08-30T02:59:26.205421+00:00, confidence not recorded.
  - readme: https://github.com/AppliedDataSciencePartners/DeepReinforcementLearning (fetched 2026-08-28T04:06:07.814301+00:00, sha b6e032046e00)
- Data as of 2026-08-30T08:39:29.467469+00:00.