/// <summary>
/// Defines the decision-making logic of the agent. Given the information
/// about the agent, returns a vector of actions.
/// </summary>
/// <returns>Vector action vector.</returns>
/// <param name="vectorObs">The vector observations of the agent.</param>
/// <param name="visualObs">The cameras the agent uses for visual observations.</param>
/// <param name="reward">The reward the agent received at the previous step.</param>
/// <param name="done">Whether or not the agent is done.</param>
/// <param name="memory">
/// The memories stored from the previous step with
/// <see cref="MakeMemory(List{float}, List{Texture2D}, float, bool, List{float})"/>
/// </param>
public override float[] Decide(List <float> vectorObs, List <Texture2D> visualObs, float reward, bool done, List <float> memory)
{
if (chessGame == null)
{
chessGame = FindObjectOfType <ChessGame> ();
}
if (academy == null)
{
academy = FindObjectOfType <ChessAcademy> ();
}
else
{
if (academy.resetParameters.ContainsKey("ai-depth"))
{
depth = Mathf.FloorToInt(academy.resetParameters["ai-depth"]);
}
}
currentTeam = chessGame.GetChess().currentTeam;
Move bestmove = ChessAI.GetBestMove(ObservationToChess(vectorObs), depth);
float[] act = new float[brainParameters.vectorActionSize.Length];
act[0] = MoveToIndex(bestmove);
chessGame.GetChess().currentTeam = currentTeam;
return(act);
}
private void AIMove(Team team)
{
if (chessGame.GetChess().currentTeam == team)
{
Move move = ChessAI.GetBestMove(chessGame.GetChess(), depth);
chessGame.GetChess().currentTeam = team;
chessGame.MakeMove(move);
}
}
