# PWhiddy/PokemonRedExperiments

Playing Pokemon Red with Reinforcement Learning

Repository: https://github.com/PWhiddy/PokemonRedExperiments
Canonical: https://ross.abutalabs.com/products/pokemonredexperiments
Language: Jupyter Notebook
License: MIT
License Family: permissive
Last push: 2025-08-28T18:15:47+00:00

## Health v2 (maintenance only)
Score: 50/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 39, release rhythm 35, longevity 100
- inputs: {"age_days": 2443, "days_push": 370, "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 7895, forks 783 (observed 2026-08-28T04:10:08.854751+00:00)

## What it is
A collection of experiments and scripts for training reinforcement learning agents to play Pokemon Red using a Game Boy emulator and PyTorch. It includes pretrained models, interactive demos, and live training broadcast tooling.

## Use cases
- train an RL agent to play Pokemon Red
- run a pretrained AI to play a Game Boy game interactively
- learn reinforcement learning with a fun game environment
- stream RL training progress to a live map visualization
- experiment with exploration reward shaping in games

## When to choose
- you want a hands-on RL project with a nostalgic game environment
- you need a reproducible baseline for deep RL experimentation
- you want to visualize agent exploration in real time

## When to avoid
- you need a production-ready game AI system
- you cannot legally obtain a Pokemon Red ROM
- you want a general-purpose game RL framework rather than a single-game experiment

## Facets
- artifact type: learning-resource
- maturity: active
- function: reinforcement-learning, machine-learning, simulation, game
- domain: reinforcement-learning, machine-learning, gaming-tools, artificial-intelligence
- platform: python, windows
- tags: pokemon-red, gameboy-emulator, pytorch, jupyter-notebook, rl-training, experiment, linux, macos, gpu

## Member repositories
- PWhiddy/PokemonRedExperiments (main) score 50

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:10:08.854751+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 2026-08-29T17:33:26.447039+00:00, confidence not recorded.
  - readme: https://github.com/PWhiddy/PokemonRedExperiments (fetched 2026-08-28T04:10:08.854751+00:00, sha 9109459ce4ca)
- Data as of 2026-08-30T08:39:29.467469+00:00.