/// <summary>
/// Initialize some learning and technical variables and ready the bot for learning.
/// Has to be called before Learn()
/// </summary>
/// <param name="control"></param>
/// <param name="player">The player that the network will be playing</param>
public override void Setup(GameControlBase control, GameControlBase.Players player)
{
base.Setup(control, player);
rand = new Random();
if (IsMultidimensionalOutput) // If the input is only the state
{
Dimensions = new List <int> {
control.FeatureNum, 110, 50, control.ActionNum
}
}
;
else // If the input is the state and action
{
Dimensions = new List <int> {
control.FeatureNum + control.ActionNum, 110, 50, 1
}
};
if (NeuralNet == null) // If the neural network wasn't loaded in another way
{
NeuralNet = new NetworkVectors(Dimensions);
}
OldNeuralNet = (NetworkVectors)NeuralNet.Clone();
Control = control;
BotTurn = player;
ReplayMem = new List <Transition>();
Epsilon = 1;
}
/// <summary>
/// Initialize some learning and technical variables and ready the bot for learning.
/// Has to be called before Learn()
/// </summary>
/// <param name="control"></param>
/// <param name="player">The player that the network will be playing</param>
public override void Setup(GameControlBase control, GameControlBase.Players player)
{
base.Setup(control, player);
Control = control;
if (Q_Table == null)
{
Q_Table = new double[control.StateNum, control.ActionNum];
}
BotTurn = player;
ActionNum = control.ActionNum;
StateNum = control.StateNum;
}
/// <summary>
/// Get the <c>LearningBot</c> ready to <c>Learn()</c>
/// </summary>
/// <param name="player">The turn the <c>LearningBot</c> will take</param>
public virtual void Setup(GameControlBase control, GameControlBase.Players player)
{
IsSetup = true;
}
