// Simulates a game to the end (game over) based on the default policy
// Returns the game over state
private State SimulateGame(State state, int POLICY)
{
if (POLICY == RANDOM_POLICY)
{
while (state.GetMoves().Count != 0)
{
state = state.ApplyMove(state.GetRandomMove());
}
return state;
}
else if (POLICY == BEST_EVAL_POLICY)
{
List<Move> moves = state.GetMoves();
while (moves.Count != 0)
{
// random move for computer
if (state.Player == GameEngine.COMPUTER)
{
state = state.ApplyMove(state.GetRandomMove());
}
else
{
// find the move that results in the best child state, based on the evaluation function
State bestState = null;
double bestScore = Double.MinValue;
foreach (Move move in moves)
{
State result = state.ApplyMove(move);
double score = AI.Evaluate(result);
if (score > bestScore)
{
bestScore = score;
bestState = result;
}
}
state = bestState;
}
moves = state.GetMoves();
}
return state;
}
else if (POLICY == EXPECTIMAX_POLICY)
{
Expectimax expectimax = new Expectimax(gameEngine, 2);
while (state.GetMoves().Count != 0) {
if (state.Player == GameEngine.COMPUTER)
{
state = state.ApplyMove(state.GetRandomMove());
}
else
{
Move move = expectimax.ExpectimaxAlgorithm(state, 2, weights);
state = state.ApplyMove(move);
}
}
return state;
}
else throw new Exception();
}
