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- Reorganized the constructors in Move.java.

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- Made the getBoardState method in Referee.java static.
- Created NeuralNetworkPlayer.java, though I don't know how to make the computer use it.
- Updated the Player interface to include passing the PlayerModel. Most of the current com agents ignore the data, but it is now available.
- Updated the train function in PlayerModel.java.
This commit is contained in:
Marshall
2012-04-29 14:19:10 -04:00
parent a756aa242d
commit 60a842d729
10 changed files with 201 additions and 94 deletions
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56
src/model/Referee.java
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@@ -20,12 +20,32 @@ public class Referee implements Runnable {
public static final String PLAYER_TURN = "Waiting for the player's move.";
public static boolean[] getBoardState(Board brd) {
boolean[] boardState = new boolean[(Board.NUM_COLS * Board.NUM_ROWS * (Board.TileColor
.values().length - 1))];
int i = 0;
for (int r = 0; r < Board.NUM_ROWS; r++) {
for (int c = 0; c < Board.NUM_COLS; c++) {
boardState[i] = (brd.getTile(r, c) == TileColor.BLUE);
boardState[i + 1] = (brd.getTile(r, c) == TileColor.GREEN);
boardState[i + 2] = (brd.getTile(r, c) == TileColor.RED);
boardState[i + 3] = (brd.getTile(r, c) == TileColor.YELLOW);
i += 4;
}
}
return boardState;
}
private Board board;
private BoardPanel boardPanel;
private final Player computerPlayer;
private final Player computerPlayer;
private final HumanPlayer humanPlayer = new HumanPlayer();
private final MainFrame mf;
private PlayerModel playerModel = null;
public Referee(MainFrame mnFrm, String player, Player computerPlayer) {
@@ -43,24 +63,6 @@ public class Referee implements Runnable {
initGame();
}
public boolean[] getBoardState() {
boolean[] boardState = new boolean[getPlayerModel().getNumInputNodes()];
int i = 0;
for (int r = 0; r < Board.NUM_ROWS; r++) {
for (int c = 0; c < Board.NUM_COLS; c++) {
boardState[i] = (board.getTile(r, c) == TileColor.BLUE);
boardState[i + 1] = (board.getTile(r, c) == TileColor.GREEN);
boardState[i + 2] = (board.getTile(r, c) == TileColor.RED);
boardState[i + 3] = (board.getTile(r, c) == TileColor.YELLOW);
i += 4;
}
}
return boardState;
}
public Player getComputerPlayer() {
return computerPlayer;
}
@@ -162,26 +164,30 @@ public class Referee implements Runnable {
System.out
.println("Interrupted while waiting for human to move!");
} else {
Move mv = humanPlayer.getMove(board);
Move mv = humanPlayer.getMove(board, playerModel);
if (board.getTile(mv.getCell().r, mv.getCell().c) == TileColor.NONE) {
playToken(humanPlayer.getMove(board));
playToken(humanPlayer.getMove(board, playerModel));
getPlayerModel().train(getMoveArray(mv));
getPlayerModel().train(getBoardState(board),
getMoveArray(mv));
} else {
humanPlayer.denyMove();
}
}
} else {
Move mv = computerPlayer.getMove(board);
Move mv = computerPlayer.getMove(board, getPlayerModel());
playToken(mv);
// TODO
// This is the call that gets a prediction of a user's move.
// Some changes will probably be necessary to put it in the
// right place and also to get the node weights. But... all in
// due time.
getPlayerModel().getOutputNodes(getBoardState());
// UPDATE: I made a neural network agent. This call is in there
// now. If the current agent doesn't use the neural network,
// then this doesn't need to be called. It will just train on
// random, meaningless data.
// getPlayerModel().getOutputNodes(getBoardState(board));
}
mf.updateMessage(getMessage());