# ntasfi/PyGame-Learning-Environment

PyGame Learning Environment (PLE) -- Reinforcement Learning Environment in Python.

Repository: https://github.com/ntasfi/PyGame-Learning-Environment
Canonical: https://ross.abutalabs.com/products/pygame-learning-environment
Language: Python
License: MIT
License Family: permissive
Topics: reinforcement-learning, game, artificial-intelligence, agent, machine-learning, pygame, python, deep-reinforcement-learning, ai, research
Last push: 2022-01-19T22:19:02+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": 3844, "days_push": 1687, "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 1065, forks 229 (observed 2026-08-28T04:03:26.700510+00:00)

## What it is
PyGame Learning Environment (PLE) is a Python library providing a reinforcement learning environment with a suite of PyGame-based games, mimicking the Arcade Learning Environment interface. It lets practitioners focus on designing models and experiments rather than building environments.

## Use cases
- train reinforcement learning agents on simple games in python
- benchmark rl algorithms without building custom environments
- get pixel observations and rewards from pygame games for deep rl
- teach reinforcement learning with an arcade-like api
- run rl experiments headless on a server

## When to choose
- you want an ALE-style interface in pure Python with easy installation
- you need simple 2D games for prototyping rl models
- you want readable, modifiable game environments for research

## When to avoid
- you need high-performance or massively parallel environments
- you require modern maintained alternatives like Gymnasium with rich env support
- you need 3D or complex simulated environments

## Facets
- artifact type: library
- maturity: maintenance
- function: reinforcement-learning, simulation, game-engine
- domain: reinforcement-learning, artificial-intelligence, machine-learning
- platform: python, windows
- tags: pygame, arcade-learning-environment, rl-environment, research, games, game-development, linux, macos

## Member repositories
- ntasfi/PyGame-Learning-Environment (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:03:26.700510+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 2026-08-30T06:55:31.125644+00:00, confidence not recorded.
  - readme: https://github.com/ntasfi/PyGame-Learning-Environment (fetched 2026-08-28T04:03:26.700510+00:00, sha 164c94717096)
- Data as of 2026-08-30T08:39:29.467469+00:00.