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));
}
