/// <summary>
/// Creates a new Q-learner
/// </summary>
/// <param name="qValueTable">The Q-value table</param>
/// <param name="selectionPolicy">The selection policy</param>
/// <param name="alpha">The learning rate</param>
/// <param name="gamma">The discount factor</param>
/// <param name="random">The random generator</param>
public QLearning(QValueTable <TState> qValueTable, IActionSelectionPolicy selectionPolicy, double alpha, double gamma, Random random)
{
if (alpha < 0)
{
throw new ArgumentException("Expected alpha to be >= 0.", "alpha");
}
if (gamma < 0)
{
throw new ArgumentException("Expected gamma to be >= 0.", "gamma");
}
this.qValueTable = qValueTable;
this.selectionPolicy = selectionPolicy;
this.alpha = alpha;
this.gamma = gamma;
this.random = random;
}
/// <summary>
/// Creates a new Q-learner using the given selection policy
/// </summary>
/// <param name="numActions">The number of actions</param>
/// <param name="selectionPolicy">The selection policy</param>
/// <param name="alpha">The learning rate</param>
/// <param name="gamma">The discount factor</param>
/// <param name="random">The random generator</param>
public static QLearning <TState> New(int numActions, IActionSelectionPolicy selectionPolicy, double alpha, double gamma, Random random)
{
return(new QLearning <TState>(new QValueTable <TState>(numActions), selectionPolicy, alpha, gamma, random));
}
