RL environment
By Gareth HoyleLast updated How we research
An RL environment is a software setting in which an AI agent takes actions, receives observations and gets a reward, so it can be trained or tested by trial.
An RL environment is a controlled software setting where an agent observes a state, takes an action and receives a new state and a reward. In reinforcement learning the agent improves by repeating tasks in the environment and learning which actions earn higher rewards.
What does an RL environment look like for AI agents?
An RL environment for language model agents is usually a sandboxed copy of a real tool or workflow, such as a code repository with tests, a browser, a spreadsheet or a ticketing system. It comes with a set of tasks and an automatic grader that checks whether each task was done, and the grader supplies the reward.
Who sells RL environments?
RL environments are sold by specialist builders and marketplaces. DataVendor, run by HUD, lists HUD tasksets and RL environments alongside code repositories. A company's own software and workflows can be the basis of one, provided live data and credentials are replaced.
See agent trajectory, evaluation dataset and agent trajectories and RL environments explained.