/// <summary>
/// Executes the given action for the given agent
/// </summary>
/// <param name="agent">The agent</param>
/// <param name="actionNumber">The index of the action</param>
/// <param name="episode">The current episode number</param>
public override void Execute(StatelessAgent <MultiArmedBanditEnvironment> agent, int actionNumber, int episode)
{
double reward = this.CalculateReward(actionNumber);
this.TotalReward += reward;
agent.Reward(reward, false, episode);
}
/// <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();
}
//Not used
public override EmptyState GetDefaultState(StatelessAgent <MultiArmedBanditEnvironment> agent)
{
throw new NotImplementedException();
}
