/// <summary>
/// Given a start state, performs a random sampling of it to terminal states and determines which available action is best to take.
/// </summary>
/// <param name="state">The state to start in.</param>
/// <param name="samples">The number of samples to take for each actino.</param>
/// <param name="cloner">The means by which duplicate states may be generated.</param>
/// <param name="action_enumerator">Enumerates the actions available in any given state.</param>
/// <param name="updater">Takes a state and an action and applies one to the other.</param>
/// <param name="state_hueristic">Determines how good a terminal state is.</param>
/// <returns>Returns the best action to take.</returns>
public static A Search(S state, int samples, StateCloner <S> cloner, ActionEnumerator <A, S> action_enumerator, ActionApplier <A, S> updater, StateEvaluator <S> state_hueristic)
{
List <A> actions = new List <A>();
List <int> expected_values = new List <int>();
// Get all the actions
IEnumerator <A> acts = action_enumerator(state);
while (acts.MoveNext())
{
actions.Add(acts.Current);
}
// If we have no actions, we can't do anything
if (actions.Count == 0)
{
return(default(A));
}
// If we have one action, don't waste time
if (actions.Count == 1)
{
return(actions[0]);
}
// Obtain the expected value of each action
foreach (A a in actions)
{
expected_values.Add(Explore(updater(cloner(state), a), samples, cloner, action_enumerator, updater, state_hueristic));
}
// Find the best action
int max = expected_values[0];
List <A> best = new List <A>();
best.Add(actions[0]);
for (int i = 1; i < expected_values.Count; i++)
{
if (expected_values[i] > max)
{
max = expected_values[i];
best.Clear();
best.Add(actions[i]);
}
else if (expected_values[i] == max)
{
best.Add(actions[i]);
}
}
return(best[rand.Next(0, best.Count)]);
}
/// <summary>
/// Takes a state and returns the expected value (pre-division) of its outcome.
/// </summary>
/// <param name="state">The state to start in.</param>
/// <param name="samples">The number of samples to take for each actino.</param>
/// <param name="cloner">The means by which duplicate states may be generated.</param>
/// <param name="action_enumerator">Enumerates the actions available in any given state.</param>
/// <param name="updater">Takes a state and an action and applies one to the other.</param>
/// <param name="state_hueristic">Determines how good a terminal state is.</param>
/// <returns>Returns the expected value (pre-division) of the state's outcome.</returns>
private static int Explore(S state, int samples, StateCloner <S> cloner, ActionEnumerator <A, S> action_enumerator, ActionApplier <A, S> updater, StateEvaluator <S> state_hueristic)
{
int ret = 0;
for (int i = 0; i < samples; i++)
{
ret += PlayToCompletion(cloner(state), action_enumerator, updater, state_hueristic);
}
return(ret);
}
