RealmOA Library

oa::ml::policy::evaluate_tanh_normal

Differentiably evaluate a stored pre-tanh normal action. Log standard deviation is clamped to [-20, 2]. Actions are mapped from [-1, 1] into [minimum, maximum]; log probability includes the donor tanh Jacobian correction, while entropy is the unsquashed diagonal-normal value. raw_action and value are zero-copy semantic output aliases. Errors Returns an error when mean, log-standard-deviation, and raw-action are not matching nonempty FP32 [E,A] matrices, value is not FP32 [E], inputs do not share one engine, bounds/epsilon are invalid, a size exceeds the shader ABI, or composite lowering fails.

FnRust public API

Function Documentation

pub fn evaluate_tanh_normal( mean: &Matrix, log_stddev: &Matrix, raw_action: &Matrix, value: &Matrix, minimum: f32, maximum: f32, epsilon: f32 ) -> Result<ContinuousPolicyResult>
Differentiably evaluate a stored pre-tanh normal action. Log standard deviation is clamped to [-20, 2]. Actions are mapped from [-1, 1] into [minimum, maximum]; log probability includes the donor tanh Jacobian correction, while entropy is the unsquashed diagonal-normal value. raw_action and value are zero-copy semantic output aliases. Errors Returns an error when mean, log-standard-deviation, and raw-action are not matching nonempty FP32 [E,A] matrices, value is not FP32 [E], inputs do not share one engine, bounds/epsilon are invalid, a size exceeds the shader ABI, or composite lowering fails.