/// <summary>
/// Creates the learner
/// </summary>
protected override ILearningAlgorithm <MouseState> CreateLearner()
{
double alpha = 1;
double gamma = 0.1;
int stopDecayAt = (int)(0.9 * this.Environment.Config.MaxEpisodes);
double epsilon = 0.4;
var selectionPolicy = new EGreedy(
epsilon,
this.Environment.Config.Random,
DecayHelpers.ConstantDecay(0, stopDecayAt, epsilon, 0));
return(QLearning <MouseState> .New(
Enum.GetValues(typeof(MouseAction)).Length,
selectionPolicy,
alpha,
gamma,
this.Environment.Config.Random));
//return Sarsa<MouseState>.New(
// Enum.GetValues(typeof(MouseAction)).Length,
// selectionPolicy,
// alpha,
// gamma,
// this.Environment.Config.Random);
}
/// <summary>
/// Runs the example
/// </summary>
public static void Run()
{
var slotMachines = new List <SlotMachine>();
slotMachines.Add(new SlotMachine(20, 120));
slotMachines.Add(new SlotMachine(5, 100));
slotMachines.Add(new SlotMachine(40, 150));
slotMachines.Add(new SlotMachine(25, 130));
slotMachines.Add(new SlotMachine(25, 120));
slotMachines.Add(new SlotMachine(60, 120));
var random = new Random(1337);
int trainingEpisodes = 10000;
double decayRatio = 0.4;
var environment = new MultiArmedBanditEnvironment(new Configuration(trainingEpisodes, random), slotMachines);
var agent = new StatelessAgent <MultiArmedBanditEnvironment>(env =>
{
double alpha = 0.05;
double gamma = 0.1;
int stopDecayAt = (int)(decayRatio * env.Config.MaxEpisodes);
double epsilon = 0.1;
var selectionPolicy = new EGreedy(
epsilon,
env.Config.Random,
DecayHelpers.ConstantDecay(0, stopDecayAt, epsilon, 0));
return(QLearning <EmptyState> .New(
slotMachines.Count,
selectionPolicy,
alpha,
gamma,
env.Config.Random));
});
environment.AddAgent(agent);
environment.Initialize();
for (int episode = 0; episode < environment.Config.MaxEpisodes; episode++)
{
environment.Reset(episode);
environment.Update(episode);
}
Console.WriteLine(string.Format("Total reward: {0}", environment.TotalReward));
Console.ReadLine();
}
