RL environment

A controlled, simulated task space where an AI agent practices completing goals and gets scored on the result. A production RL environment has three parts: a replica of real software or a real task domain, goal specifications usually written by domain experts, and a verifier that decides whether the agent succeeded. Environments train how a model acts; they do not replace the real-world data that teaches a model what the world is.