{"categories":[{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence"}],"enrichment":{"capability":"Gymnasium provides a standard Python API for building and testing reinforcement learning algorithms against a collection of environments ranging from simple toy problems to complex physics simulations and Atari games.","skillfed_tags":["reinforcement-learning","benchmarking","simulation"],"use_cases":["Prototyping and testing new reinforcement learning algorithms against standard benchmarks like CartPole or MuJoCo control tasks","Training agents on Atari 2600 games for research into deep RL methods","Debugging RL implementations using simple discrete environments with small state/action spaces","Building multi-agent systems via compatible third-party environments like PettingZoo","Reproducing published RL research by leveraging strict environment versioning"],"what_it_does":"Gymnasium is the maintained successor to OpenAI's Gym library, providing a standardized interface for reinforcement learning research and algorithm development. It defines how learning agents interact with environments through a simple API: agents observe state, take actions, and receive rewards. The library includes built-in environment families (Classic Control, Box2D, Toy Text, MuJoCo, Atari) and supports third-party environments that follow its API contract.\n\nThe package is built on numpy, cloudpickle, typing-extensions, and farama-notifications, keeping dependencies minimal for the base install. Environments are versioned strictly (e.g., CartPole-v1) to ensure reproducibility when algorithm results might be affected by environment changes. It is actively developed, production-stable, and widely used in the RL community for benchmarking and prototyping learning algorithms.","worth_installing":"Yes. Gymnasium is the de facto standard RL environment interface in Python, actively maintained, production-stable, and has no known vulnerabilities. Install friction is low, dependencies are minimal, and the MIT license imposes no restrictions. Essential for anyone working in reinforcement learning research or algorithm development."},"id":"gymnasium","links":{"html":"https://skillfed.io/packages/gymnasium","md":"https://skillfed.io/packages/gymnasium.md","pypi":"https://pypi.org/project/gymnasium/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-04-22","license_spdx":null,"license_treatment":"permissive","name":"gymnasium","python_support":"supports_current","summary":"A standard API for reinforcement learning and a diverse set of reference environments (formerly Gym)."},"popularity":{"monthly_downloads":8352518,"position":1632,"tier":"top_5000"},"security":{"n_vulnerabilities":0},"version":"1.3.0"}