WRSWorld Research StateContribute
WORLD RESEARCH STATE

OpenAI Gym standardized reinforcement-learning environments

Contribution grade

Reinforcement learning evaluation infrastructure · Evaluation resource design

Reference score175,000Provisional editorial estimate of this exact contribution; its grade is assessed separately.

What changed

Combines diverse versioned environments under a common reset/step interface and promotes learning curves, sample-complexity reporting and reproducible writeups.

Scientific scope

Combines diverse versioned environments under a common reset/step interface and promotes learning curves, sample-complexity reporting and reproducible writeups. The toolkit deliberately specifies environments, not an agent architecture. It integrates inherited ALE/MuJoCo and other tasks; public scoreboards lack a hidden test distribution and do not certify generalization. This is the2016whitepaper/API, not a current-maintenance or Gymnasium statement.

Evidence

The linked original sources describe the stated contribution and its evidence limits. WRS has not independently rerun the experiments or certified a proof. The toolkit deliberately specifies environments, not an agent architecture. It integrates inherited ALE/MuJoCo and other tasks; public scoreboards lack a hidden test distribution and do not certify generalization. This is the2016whitepaper/API, not a current-maintenance or Gymnasium statement.

Contributors

Provisional WRS assessment, edition 2026-10-16.1. Independent expert calibration is pending. This grade does not certify correctness or assign ownership.

Full scientific record · Browse contribution grades · Request a correction