int main(void){ int units; printf("How many pounds to a firkin of butter?\n"); scanf("%d", &units); while ( units != 56) critic(&units); printf("You must have looked it up!\n"); return 0; }
void GPolynomialSingleLabel::train(GMatrix& features, GMatrix& labels) { GAssert(labels.cols() == 1); init(features.cols()); GPolynomialRegressCritic critic(this, features, labels); //GStochasticGreedySearch search(&critic); GMomentumGreedySearch search(&critic); search.searchUntil(100, 30, .01); setCoefficients(search.currentVector()); fromBezierCoefficients(); }
int main() { extern int units; scanf("%d", &units); while (units != 56) { critic(); } printf("You must have looked it up!\n"); return 0; }
int main() { extern int units;//同一文件的可选二次声明 printf("How many pounds to a frikin of butter?\n"); scanf("%d",&units); while(units != 56) { critic(); } printf("You must have looked it up.\n"); return 0; }
int main(void) { extern int units; /* an optional redeclaration */ printf("How many pounds to a firkin of butter?\n"); scanf("%d", &units); while (units != 56) critic(); printf("You must have looked it up!\n"); return 0; }
std::unique_ptr<ARACAgent> FactoryOfAgents::makeARACAgent() const { // State-value function critic LinearRegressor linearRegV(dimObservation); // Initialize critics Critic critic(linearRegV); // Boltzmann Policy std::vector<double> possibleAction {-1.0, 1.0}; BoltzmannPolicy policy(dimObservation, possibleAction); // Stochastic Actor StochasticActor actor(policy); // ARAC Agent return std::unique_ptr<ARACAgent>(new ARACAgent(actor, critic, *baselineLearningRatePtr, *criticLearningRatePtr, *actorLearningRatePtr, lambda)); }
std::unique_ptr<ARACAgent> FactoryOfAgents::makePGPEAgent() const { // State-value function critic LinearRegressor linearRegV(dimObservation); // Initialize critics Critic critic(linearRegV); // Binary policy BinaryPolicy controller(dimObservation); GaussianDistribution distribution(controller.getDimParameters()); PGPEPolicy policy(controller, distribution, 1.0); // Stochastic Actor StochasticActor actor(policy); // ARAC Agent return std::unique_ptr<ARACAgent> (new ARACAgent(actor, critic, *baselineLearningRatePtr, *criticLearningRatePtr, *actorLearningRatePtr, lambda)); }