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Schulman and colleagues: trust region policy optimization

Contribution grade

Reinforcement-learning optimization · Human-designed research method or resource

Reference score150,000Provisional editorial estimate of this exact contribution; its grade is assessed separately.

What changed

Developed a policy-improvement surrogate and practical constrained-update approximation for optimizing large nonlinear policies.

Scientific scope

Developed a policy-improvement surrogate and practical constrained-update approximation for optimizing large nonlinear policies. The theoretical guarantee applies to the specified surrogate scheme; practical TRPO makes approximations. Empirical monotonic behavior is not a universal guarantee and is distinct from later PPO.

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 theoretical guarantee applies to the specified surrogate scheme; practical TRPO makes approximations. Empirical monotonic behavior is not a universal guarantee and is distinct from later PPO.

Contributors

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

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