{"categories":[{"label":"Scientific/Engineering","url":"https://skillfed.io/packages/category/scientific-engineering/5"},{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence/6"}],"enrichment":{"capability":"ale-py provides a Python interface to the Atari 2600 emulator Stella, allowing you to develop and test reinforcement learning agents on classic Atari games with automatic score extraction and game-over detection.","skillfed_tags":["reinforcement-learning","atari-benchmark","emulation"],"use_cases":["Train deep reinforcement learning agents on Atari games as a standard RL benchmark.","Benchmark policy gradient or Q-learning algorithms against a large suite of games.","Develop and test game-playing agents with automatic score tracking and episode termination.","Run vectorized parallel environments for faster training of multiple Atari game instances.","Create interactive browser-based RL demos or educational tools via WebAssembly."],"what_it_does":"ale-py is a Python wrapper around the Stella Atari 2600 emulator, designed to provide a research-grade platform for developing and benchmarking reinforcement learning agents. It decouples emulation from rendering and sound, enabling fast headless training while also supporting visual output when needed. The package includes Atari ROMs, automatic game-over detection, and score extraction for over 100 games, making it a turnkey environment for RL research without requiring separate ROM management.\n\nThe package integrates natively with Gymnasium (the maintained successor to OpenAI Gym), so you can use it either through ale-py's direct ALEInterface API or through Gymnasium's standard environment interface. It also provides a C++-based vectorizer for parallel environment execution and WebAssembly support for browser-based experiments. Runtime dependencies are minimal: only numpy and typing-extensions.","worth_installing":"Yes, with conditions. ale-py is actively maintained, widely used in RL research, and has no known vulnerabilities. Install it if you are developing RL agents and need a standard Atari benchmark environment. Be aware of the GPL-2.0-only copyleft license if you plan proprietary use, and ensure your Python version is 3.10 or later (free-threaded CPython is not supported)."},"id":"ale-py","links":{"html":"https://skillfed.io/packages/ale-py","md":"https://skillfed.io/packages/ale-py.md","pypi":"https://pypi.org/project/ale-py/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-05-30","license_spdx":"GPL-2.0-only","license_treatment":"copyleft","name":"ale-py","python_support":"supports_current","summary":"The Arcade Learning Environment (ALE) - a platform for AI research."},"popularity":{"monthly_downloads":298625,"position":7871,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"0.12.0"}