78 lines
2.1 KiB
C++
78 lines
2.1 KiB
C++
// Copyright Epic Games, Inc. All Rights Reserved.
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#pragma once
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#include "LearningOptimizer.h"
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#define UE_API LEARNING_API
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namespace UE::Learning
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{
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/**
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* Settings for the CMA Optimizer
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*/
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struct FCMAOptimizerSettings
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{
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// The size of the initial exploration region for CMA.
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// Larger introduces more exploration but may have worse initial results or cause longer convergence
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float InitialStepSize = 0.3f;
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// What ratio of samples are used to update the covariance matrix and how many are discarded.
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// Larger values can speed up convergence on flat gradients but will make things worse on complex loss landscapes
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float SurvialRatio = 0.5f;
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};
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/**
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* CMA Optimizer
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*
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* Black box optimizer based on Covariance Matrix Adaption
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*/
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struct FCMAOptimizer : public IOptimizer
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{
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UE_API FCMAOptimizer(const uint32 Seed, const FCMAOptimizerSettings& InSettings = FCMAOptimizerSettings());
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UE_API virtual void Resize(const int32 SampleNum, const int32 DimensionsNum) override final;
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UE_API virtual void Reset(
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TLearningArrayView<2, float> OutSamples,
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const TLearningArrayView<1, const float> InitialGuess) override final;
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UE_API virtual void Update(
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TLearningArrayView<2, float> InOutSamples,
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const TLearningArrayView<1, const float> Losses,
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const ELogSetting LogSettings = ELogSetting::Normal) override final;
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static UE_API int32 DefaultSampleNum(const int32 DimensionNum);
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private:
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UE_API void Sample(TLearningArrayView<2, float> OutSamples);
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uint32 Seed = 0;
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FCMAOptimizerSettings Settings;
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int32 Iterations = 0;
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float Sigma = 0.3f;
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TLearningArray<1, float> PathSigma;
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TLearningArray<1, float> PathCovariance;
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TLearningArray<1, float> Mean;
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TLearningArray<2, float> Covariance;
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int32 Mu = 0;
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float MuEff = 0.0f;
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TLearningArray<1, float> Weights;
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TLearningArray<1, float> OldMean;
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TLearningArray<1, int32> LossRanking;
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TLearningArray<1, float> UpdateDirection;
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TLearningArray<2, float> CovarianceTransform;
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TLearningArray<2, float> CovarianceInverseSqrt;
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TLearningArray<2, float> GaussianSamples;
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};
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}
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#undef UE_API
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