rl-environments

What problem does it solve?

This Skill helps developers design, validate, and debug custom Gym/Gymnasium environments, including spaces, wrappers, and vectorized configurations, to accelerate RL experiments and ensure API compatibility.

Core Features & Use Cases

• Create and customize environments with proper action and observation spaces.
• Apply wrappers for preprocessing, rewards, time limits, and parallelization.
• Build and validate vectorized environments (DummySyncAsync) for scalable training.
• Diagnose common environment issues (reset/step semantics, space mismatches, termination conditions) and migrate code between Gym and Gymnasium.
• Use as a foundational blueprint for teaching or researching RL environment design patterns.

Quick Start

Use the rl-environments skill to scaffold a minimal CartPole-like environment, define discrete actions and Box observations, implement reset and step, then wrap with a TimeLimit and run a simple random-agent loop to verify shapes and termination.