final

go_player/Makefile

@@ -0,0 +1,29 @@
+files += tp_player-ndacremont_meyben/README.md
+files += tp_player-ndacremont_meyben/scrum.pt
+files += tp_player-ndacremont_meyben/plays-8x8.json
+files += tp_player-ndacremont_meyben/localGame.py
+files += tp_player-ndacremont_meyben/namedGame.py
+files += tp_player-ndacremont_meyben/Goban.py
+files += tp_player-ndacremont_meyben/myPlayer.py
+files += tp_player-ndacremont_meyben/moveSearch.py
+files += tp_player-ndacremont_meyben/playerInterface.py
+files += tp_player-ndacremont_meyben/requirements.txt
+
+.PHONY += all
+all: tp_player-ndacremont_meyben.tar.gz
+
+tp_player-ndacremont_meyben.tar.gz: tp_player-ndacremont_meyben $(files)
+	tar -cvzf $@ $^
+
+tp_player-ndacremont_meyben/%: %
+	cp $^ $@
+
+tp_player-ndacremont_meyben:
+	mkdir -p $@
+
+.PHONY += clean
+clean:
+	$(RM) -r tp_player-ndacremont_meyben tp_player-ndacremont_meyben.tar.gz
+
+
+.PHONY: $(PHONY)

go_player/README.md

@@ -0,0 +1,5 @@
+# TP Noté joueur Go -- Nemo D'ACREMONT, Martin EYBEN, G1
+
+
+
+

go_player/localGame.py

@@ -2,7 +2,6 @@
 
 import Goban
 import myPlayer
-import gnugoPlayer
 import time
 from io import StringIO
 import sys
@@ -14,7 +13,7 @@ player1 = myPlayer.myPlayer()
 player1.newGame(Goban.Board._BLACK)
 players.append(player1)
 
-player2 = gnugoPlayer.myPlayer()
+player2 = myPlayer.myPlayer()
 player2.newGame(Goban.Board._WHITE)
 players.append(player2)
 

go_player/moveSearch.py

@@ -4,85 +4,74 @@ import math
 from typing import Any, Callable
 import Goban
 
-
-# Returns heuristic, move
 def _alphabeta(
     board: Goban.Board,
     heuristic: Callable[[Goban.Board, Any], float],
     color,
-    move,
     alpha=-math.inf,
     beta=math.inf,
     depth: int = 3,
-    shouldStop = lambda: False
+    shouldStop=lambda: False
 ) -> tuple[float, Any]:
+    if board.is_game_over() or depth == 0:
+        return heuristic(board, color), None
 
-    wantMax = (board.next_player == color)
+    wantMax = (board.next_player() == color)
-    if depth == 0 or board.is_game_over():
+    best_move = None
-        return heuristic(board, board.next_player()), move
 
     if wantMax:
-        acc = -math.inf, None
+        acc = -math.inf
         for move in board.generate_legal_moves():
             if Goban.Board.flat_to_name(move) == "PASS":
                 continue
 
             board.push(move)
-            value = (
+            value = _alphabeta(
-                _alphabeta(
+                board,
-                    board,
+                heuristic=heuristic,
-                    alpha=alpha,
+                color=color,
-                    beta=beta,
+                alpha=alpha,
-                    move=move,
+                beta=beta,
-                    heuristic=heuristic,
+                depth=depth - 1,
-                    color=color,
+                shouldStop=shouldStop
-                    depth=depth - 1,
+            )[0]
-                )[0],
-                move,
-            )
-            acc = max(
-                acc,
-                value,
-                key=lambda t: t[0],
-            )
             board.pop()
 
-            if shouldStop() or acc[0] >= beta:
+            if value > acc:
+                acc = value
+                best_move = move
+
+            alpha = max(alpha, acc)
+            if shouldStop() or acc >= beta:
                 break  # beta cutoff
-            alpha = max(alpha, acc[0])
 
     else:
-        acc = math.inf, None
+        acc = math.inf
         for move in board.generate_legal_moves():
             if Goban.Board.flat_to_name(move) == "PASS":
                 continue
 
             board.push(move)
-            value = (
+            value = _alphabeta(
-                _alphabeta(
+                board,
-                    board,
+                heuristic=heuristic,
-                    alpha=alpha,
+                color=color,
-                    beta=beta,
+                alpha=alpha,
-                    move=move,
+                beta=beta,
-                    heuristic=heuristic,
+                depth=depth - 1,
-                    color=color,
+                shouldStop=shouldStop
-                    depth=depth - 1,
+            )[0]
-                )[0],
-                move,
-            )
-            acc = min(
-                acc,
-                value,
-                key=lambda t: t[0],
-            )
             board.pop()
 
-            if shouldStop() or acc[0] <= alpha:
+            if value < acc:
+                acc = value
+                best_move = move
+
+            beta = min(beta, acc)
+            if shouldStop() or acc <= alpha:
                 break  # alpha cutoff
-            beta = min(beta, acc[0])
-
-    return acc
 
+    return acc, best_move
 
 def alphabeta(
     board: Goban.Board,
@@ -90,36 +79,35 @@ def alphabeta(
     color,
     depth: int = 3,
 ):
-    _, move = _alphabeta(board, move=-1, heuristic=heuristic, color=color, depth=depth)
+    _, move = _alphabeta(board, heuristic=heuristic, color=color, depth=depth)
     return move
 
 
-def IDDFS(board: Goban.Board, heuristic, color, duration: float, maxdepth=42):
+def IDDFS(
-    st = time.time()
+    board: Goban.Board,
-    shouldStop = (lambda: time.time() - st > duration)
+    heuristic: Callable[[Goban.Board, Any], float],
-    depth = 0
+    color,
-    move = -1
+    max_depth: int = 10,
-    score = -1
+    duration: float = 5.0  # Duration in seconds
+):
+    best_move = None
+    start_time = time.time()
+    shouldStop = lambda: (time.time() - start_time) >= duration
 
-    while not shouldStop() and depth <= maxdepth:
+    for depth in range(1, max_depth + 1):
-        if depth % 2 == 0:
-            score, move = _alphabeta(
-                    board, heuristic, color, move=move, alpha=-math.inf, beta=math.inf, depth=depth, shouldStop=shouldStop
-            )
 
-            if score == math.inf:
+        value, move = _alphabeta(
-                return move, score
+            board,
+            heuristic=heuristic,
+            color=color,
+            depth=depth,
+            shouldStop=shouldStop
+        )
 
-        else:
+        if shouldStop():
-            score, move = _alphabeta(
+            break
-                board, heuristic, color, move=move, alpha=-math.inf, beta=math.inf, depth=depth, shouldStop=shouldStop
-            )
 
-            if score == -math.inf:
+        print(f"{depth}, {value}", file=stderr)
-                return move, score
+        best_move = move
 
-        print("depth:", depth, time.time() - st, score,  file=stderr)
+    return best_move
-        depth += 1
-
-    print(time.time() - st, duration, depth, file=stderr)
-    return move, score

go_player/myPlayer.py

@@ -32,6 +32,9 @@ def setup_device():
 
 
 def goban2Go(board: Goban.Board):
+    """
+    Convert a goban board to a tensor for the model
+    """
     goBoard = torch.zeros((3, 8, 8), dtype=torch.float32)
     black_plays = (board.next_player() == Goban.Board._BLACK)
 
@@ -119,33 +122,32 @@ class myPlayer(PlayerInterface):
     def __init__(self):
         self._board = Goban.Board()
         self._mycolor = None
+        self.last_op_move = -2
 
         self.device = setup_device()
-        print(self.device)
-
         self.model = GoModel().to(self.device)
-
         checkpoint = torch.load("scrum.pt", weights_only=True, map_location=self.device)
         self.model.load_state_dict(checkpoint["model_state_dict"])
-        self.last_op_move = None
 
         self.maxtime = 1800
         self.time = 0
 
+        # Load plays for the opening
         self.plays = []
         with open("plays-8x8.json") as f:
             plays = json.load(f)
 
+        # Only keep the plays we want
         l = "W" if self._mycolor == Goban.Board._WHITE else "B"
         filtered = filter(lambda t: l in t["result"], plays)
 
+        # We sort to take the most advantageous openings
         lp = l + "+"
         for el in filtered:
             el["result"] = float(el["result"].replace(lp, ""))
             self.plays.append(el)
 
         self.plays.sort(key=lambda t: t["result"])
-        self.turn = 0
 
     def getPlayerName(self):
         return "xXx_7h3_5cRuM_M45T3r_xXx"
@@ -155,7 +157,7 @@ class myPlayer(PlayerInterface):
             if board.winner() == board._EMPTY:
                 return 0.5
 
-            return math.inf if board.winner() == color else -math.inf
+            return math.inf if board.winner() == self._mycolor else -math.inf
 
         go_board = torch.from_numpy(np.array([goban2Go(board)])).float().to(self.device)
 
@@ -174,58 +176,57 @@ class myPlayer(PlayerInterface):
             print("Referee told me to play but the game is over!")
             return "PASS"
 
-        duration = 1.
+        # Take more time in endgame
-
         if self._board._nbBLACK + self._board._nbWHITE < 10:
-            duration = 1
+            duration = 5
 
-        elif self._board._nbBLACK + self._board._nbWHITE < 30:
+        elif self._board._nbBLACK + self._board._nbWHITE < 40:
-            duration = 1
+            duration = 20
 
-        elif self._board._nbBLACK + self._board._nbWHITE > 40:
+        else:
-            duration = 1 # 64 - (self._board._nbBLACK + self._board._nbWHITE)
+            duration = 30
 
+        # Play quickly if lack of time
         duration = min(duration, (self.maxtime - self.time) / 10)
+
         move = -1
-        score = 0
-
         b, w = self._board.compute_score()
-        # move = alphabeta(self._board, self.nnheuristic, self._mycolor, 1)
-        if self.last_op_move == "PASS" and (b - w) * (1 if self._mycolor == Goban.Board._BLACK else -1) > 0:
-            move = -1
-            score = math.inf
 
-        elif self._board._nbBLACK + self._board._nbWHITE < 20:
+        # If passing wins the game, pass
+        if self.last_op_move == -1 and (b - w) * (1 if self._mycolor == Goban.Board._BLACK else -1) > 0:
+            move = -1
+
+        # Play greedily opening moves early in the game
+        elif self._board._nbBLACK + self._board._nbWHITE < 10:
             turn = self._board._nbBLACK + self._board._nbWHITE
             for play in self.plays:
                 if len(play["moves"]) > turn and Goban.Board.name_to_flat(play["moves"][turn]) in self._board.legal_moves():
                     move = Goban.Board.name_to_flat(play["moves"][turn])
-                    score = 1
 
-        elif move == -1:
+        # Use iddfs alphabeta
-            move, score = IDDFS(
+        else:
-                self._board, self.nnheuristic, self._mycolor, duration=duration, maxdepth=64
+            move = IDDFS(
+                self._board, self.nnheuristic, self._mycolor, duration=duration, max_depth=64
             )
 
-        self._board.push(move)
-        print(move, score, file=stderr)
         nd = time.time()
         self.time += (nd - st)
-        self.turn += 1
 
-        # New here: allows to consider internal representations of moves
+        print(move, (nd - st), file=stderr)
-        # move is an internal representation. To communicate with the interface I need to change if to a string
+
+        self._board.push(move)
         return Goban.Board.flat_to_name(move)
 
     def playOpponentMove(self, move):
         print("Opponent played ", move)  # New here
-        # the board needs an internal represetation to push the move.  Not a string
         self._board.push(Goban.Board.name_to_flat(move))
-        self.last_op_move = move
+        self.last_op_move = Goban.Board.name_to_flat(move)
-        self.turn += 1
 
     def newGame(self, color):
+        self._board = Goban.Board()
         self._mycolor = color
+        self.last_op_move = -2
+        self.time = 0
 
     def endGame(self, winner):
         if self._mycolor == winner: