Does it support parallel or vectorized training?

Absolutely. It provides specific implementation patterns for vectorizing environments using Gymnasium native tools, Stable-Baselines3, and PufferLib for high-performance training.

What is the gym skill for?

It provides a standardized framework and catalog for Gymnasium-compatible environments used in training reinforcement learning agents across various scientific and gaming domains.

Which simulation domains does this skill support?

It supports physics (MuJoCo, PyBullet), robotics (Isaac Gym), energy systems (Microgrids), chemistry (Molecular synthesis), and classic arcade games (Atari, Doom).

Reinforcement Learning Gyms

Name: Reinforcement Learning Gyms
Author: plurigrid

byplurigrid

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Data Science & ML

Standardizes and catalogs diverse Gymnasium and OpenAI Gym environments for reinforcement learning across physics, robotics, chemistry, and gaming domains.

This skill provides a unified interface and comprehensive catalog for Gymnasium (formerly OpenAI Gym) environments, serving as a foundational substrate for agent learning and reinforcement learning (RL) research. It bridges the gap between theoretical RL and practical implementation by organizing environments into a clear taxonomy—including physics simulations, robotics, energy systems, and molecular chemistry—while offering standardized code patterns for custom environment creation, high-throughput vectorization, and sophisticated reward wrapping. Whether you are training agents for MuJoCo physics or specialized chemical synthesis, this skill provides the necessary geometric and functional structures to manage agent-environment interactions effectively.

Key Features

01Standardized Gymnasium API templates for building custom observation and action spaces

02Integrated reward wrappers and observation normalization tools for stable agent training

03Built-in support for high-performance vectorized environments including PufferLib and SB3

048 GitHub stars

05Domain-specific bridges for complex simulations like microgrids and molecular synthesis

06Unified taxonomy for RL environments across physics, robotics, energy, chemistry, and gaming

Use Cases

01Developing custom training environments for specialized domains such as DeFi markets or chemical engineering

02Benchmarking Reinforcement Learning algorithms across diverse simulation backends like MuJoCo and Isaac Gym

03Implementing high-throughput training pipelines using vectorized simulation wrappers for massive parallelization

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add plurigrid/asi gym

For use in Claude.ai and ChatGPT

Download Skill