go_player/.gitignore

@@ -4,5 +4,3 @@ __pycache__
 /go-package.tgz
 *.ipynb
 /chess
-/tp_player-ndacremont_meyben
-/tp_player-ndacremont_meyben.tar.gz

go_player/Makefile

@@ -1,27 +0,0 @@
-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: $(files)
-	tar -cvzf $@ $^
-
-tp_player-ndacremont_meyben/%: %
-	@mkdir -p $(dir $@)
-	cp $^ $@
-
-.PHONY += clean
-clean:
-	$(RM) -r tp_player-ndacremont_meyben tp_player-ndacremont_meyben.tar.gz
-
-
-.PHONY: $(PHONY)

go_player/README.md

@@ -1,31 +0,0 @@
-# TP Noté joueur Go -- Nemo D'ACREMONT, Martin EYBEN, G1
-
-## Fichiers nécessaires pour lancer le joueur
-
-Les fichiers suivants sont nécessaire pour lancer le joueur :
-
-* myPlayer.py
-* moveSearch.py
-* playerInterface.py
-* scrum.pt
-* plays-8x8.json
-
-## Librairies nécessaire
-
-Ces librairies sont listées dans le fichier `requirements.txt` et sont les
-suivantes :
-
-* PyTorch
-* Numpy
-
-## Techniques utilisées
-
-* IDDFS avec alphabeta
-* Stop le calcule du coup dans le parcours alphabeta si on dépasse le temps
-alloué
-* Joue des coups classiques sans heuristique lorsqu'il y a peu (<10) de pions
-sur le plateau
-* Passe si le joueur l'adversaire vient de passer et qu'on est en train de
-gagner
-* Plus de pions sont joués, plus on alloue du temps à jouer, sauf si on est
-proche de 30min, dans quel cas on joue rapidement

go_player/localGame.py

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

go_player/moveSearch.py

@@ -5,74 +5,83 @@ 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)
-    best_move = -1
+    if depth == 0 or board.is_game_over():
+        return heuristic(board, board.next_player()), move
 
     if wantMax:
-        acc = -math.inf
+        acc = -math.inf, None
         for move in board.generate_legal_moves():
             if Goban.Board.flat_to_name(move) == "PASS":
                 continue
 
             board.push(move)
-            value = _alphabeta(
+            value = (
+                _alphabeta(
                     board,
-                heuristic=heuristic,
-                color=color,
                     alpha=alpha,
                     beta=beta,
+                    move=move,
+                    heuristic=heuristic,
+                    color=color,
                     depth=depth - 1,
-                shouldStop=shouldStop,
+                )[0],
-            )[0]
+                move,
+            )
+            acc = max(
+                acc,
+                value,
+                key=lambda t: t[0],
+            )
             board.pop()
 
-            if value > acc:
+            if shouldStop() or acc[0] >= beta:
-                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
+        acc = math.inf, None
         for move in board.generate_legal_moves():
             if Goban.Board.flat_to_name(move) == "PASS":
                 continue
 
             board.push(move)
-            value = _alphabeta(
+            value = (
+                _alphabeta(
                     board,
-                heuristic=heuristic,
-                color=color,
                     alpha=alpha,
                     beta=beta,
+                    move=move,
+                    heuristic=heuristic,
+                    color=color,
                     depth=depth - 1,
-                shouldStop=shouldStop,
+                )[0],
-            )[0]
+                move,
+            )
+            acc = min(
+                acc,
+                value,
+                key=lambda t: t[0],
+            )
             board.pop()
 
-            if value < acc:
+            if shouldStop() or acc[0] <= alpha:
-                acc = value
-                best_move = move
-
-            beta = min(beta, acc)
-            if shouldStop() or acc <= alpha:
                 break  # alpha cutoff
+            beta = min(beta, acc[0])
 
-    return acc, best_move
+    return acc
 
 
 def alphabeta(
@@ -81,30 +90,36 @@ def alphabeta(
     color,
     depth: int = 3,
 ):
-    _, move = _alphabeta(board, heuristic=heuristic, color=color, depth=depth)
+    _, move = _alphabeta(board, move=-1, heuristic=heuristic, color=color, depth=depth)
     return move
 
 
-def IDDFS(
+def IDDFS(board: Goban.Board, heuristic, color, duration: float, maxdepth=42):
-    board: Goban.Board,
+    st = time.time()
-    heuristic: Callable[[Goban.Board, Any], float],
+    shouldStop = (lambda: time.time() - st > duration)
-    color,
+    depth = 0
-    max_depth: int = 10,
+    move = -1
-    duration: float = 5.0,  # Duration in seconds
+    score = -1
-):
-    best_move = -1
-    start_time = time.time()
-    shouldStop = lambda: (time.time() - start_time) >= duration
 
-    for depth in range(1, max_depth + 1):
+    while not shouldStop() and depth <= maxdepth:
-        value, move = _alphabeta(
+        if depth % 2 == 0:
-            board, heuristic=heuristic, color=color, depth=depth, shouldStop=shouldStop
+            score, move = _alphabeta(
+                    board, heuristic, color, move=move, alpha=-math.inf, beta=math.inf, depth=depth, shouldStop=shouldStop
             )
 
-        if shouldStop():
+            if score == math.inf:
-            break
+                return move, score
 
-        print(f"{depth}, {value}", file=stderr)
+        else:
-        best_move = move
+            score, move = _alphabeta(
+                board, heuristic, color, move=move, alpha=-math.inf, beta=math.inf, depth=depth, shouldStop=shouldStop
+            )
 
-    return best_move
+            if score == -math.inf:
+                return move, score
+
+        print("depth:", depth, time.time() - st, score,  file=stderr)
+        depth += 1
+
+    print(time.time() - st, duration, depth, file=stderr)
+    return move, score

go_player/myPlayer.py

@@ -9,14 +9,14 @@ from sys import stderr
 import time
 import math
 import Goban
-from moveSearch import IDDFS
+from random import choice
+from moveSearch import IDDFS, alphabeta
 from playerInterface import *
 import torch
 import torch.nn as nn
 import torch.nn.functional as F
 import numpy as np
-import json
+from torch.utils.data import Dataset
-
 
 def setup_device():
     # Allows to use the GPU if available
@@ -31,11 +31,8 @@ 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
+    black_plays = (board.next_player() == Goban.Board._BLACK)
 
     flat = board.get_board()
     for i in range(8):
@@ -45,10 +42,9 @@ def goban2Go(board: Goban.Board):
             elif flat[i * 8 + j] == Goban.Board._WHITE:
                 goBoard[1, i, j] = 1
 
-    goBoard[2, :, :] = 1 if black_plays else 0
+    goBoard[2,:,:] = 1 if black_plays else 0
 
-    # sometime, a little bit of magic is required
+    return goBoard
-    return torch.from_numpy(np.array([goBoard])).float()
 
 
 class GoModel(nn.Module):
@@ -59,29 +55,37 @@ class GoModel(nn.Module):
              nn.Conv2d(3, 16, kernel_size=3, padding=1, bias=False),
              nn.BatchNorm2d(16),
              torch.nn.ReLU(),
+
              nn.Conv2d(16, 32, kernel_size=3, padding=1, bias=False),
              nn.BatchNorm2d(32),
              torch.nn.ReLU(),
+
              nn.Conv2d(32, 64, kernel_size=3, padding=1, bias=False),
              nn.BatchNorm2d(64),
              nn.Dropout(0.4),
              torch.nn.ReLU(),
+
              nn.Conv2d(64, 128, kernel_size=3, padding=1, bias=False),
              nn.BatchNorm2d(128),
              torch.nn.ReLU(),
+
              nn.Conv2d(128, 128, kernel_size=3, padding=1, bias=False),
              nn.BatchNorm2d(128),
              torch.nn.ReLU(),
+
              nn.Conv2d(128, 128, kernel_size=3, padding=1, bias=False),
              nn.BatchNorm2d(128),
              torch.nn.ReLU(),
+
              nn.Flatten(),
+
              nn.Linear(128 * 8 * 8, 128),
              nn.BatchNorm1d(128),
              torch.nn.ReLU(),
+
              nn.Dropout(0.4),
              nn.Linear(128, 1),
-            nn.Sigmoid(),
+             nn.Sigmoid()
         )
 
     def forward(self, x):
@@ -91,9 +95,7 @@ class GoModel(nn.Module):
             y = self.net(x)
             batch_size = x.size(0)
 
-            x_rotated = torch.stack(
+            x_rotated = torch.stack([torch.rot90(x, k=k, dims=[2, 3]) for k in range(4)], dim=1) # x_rotated: [batch_size, 4, 3, 8, 8]
-                [torch.rot90(x, k=k, dims=[2, 3]) for k in range(4)], dim=1
-            )  # x_rotated: [batch_size, 4, 3, 8, 8]
             x_rotated = x_rotated.view(-1, 3, 8, 8)  # [batch_size*4, 3, 8, 8]
 
             with torch.no_grad():
@@ -116,44 +118,38 @@ 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"])
-
     def getPlayerName(self):
         return "xXx_7h3_5cRuM_M45T3r_xXx"
 
+    @staticmethod
+    def simple_heuristic(board, color):
+        # Simple stone difference heuristic
+        score = board.compute_score()
+        return (
+            score[0] - score[1] if color == Goban.Board._BLACK else score[1] - score[0]
+        )
+
     def nnheuristic(self, board: Goban.Board, color):
         if board.is_game_over():
             if board.winner() == board._EMPTY:
                 return 0.5
 
-            return math.inf if board.winner() == self._mycolor else -math.inf
+            return math.inf if board.winner() == color else -math.inf
 
-        go_board = goban2Go(board).to(self.device)
+        go_board = torch.from_numpy(np.array([goban2Go(board)])).float().to(self.device)
 
         self.model.eval()
         with torch.no_grad():
@@ -170,68 +166,47 @@ class myPlayer(PlayerInterface):
             print("Referee told me to play but the game is over!")
             return "PASS"
 
-        # Take more time in endgame
+        duration = 1.
+
         if self._board._nbBLACK + self._board._nbWHITE < 10:
+            duration = 3
+
+        elif self._board._nbBLACK + self._board._nbWHITE < 30:
             duration = 5
 
-        elif self._board._nbBLACK + self._board._nbWHITE < 40:
+        elif self._board._nbBLACK + self._board._nbWHITE > 40:
-            duration = 20
+            duration = 64 - (self._board._nbBLACK + self._board._nbWHITE)
 
-        else:
-            duration = 30
-
-        # Play quickly if lack of time
         duration = min(duration, (self.maxtime - self.time) / 10)
 
+        # move = alphabeta(self._board, self.nnheuristic, self._mycolor, 1)
+        if self.last_op_move == "PASS" and self._board.diff_stones_board() * (1 if self._mycolor == Goban.Board._BLACK else -1) > 0:
             move = -1
-        b, w = self._board.compute_score()
+            score = math.inf
 
-        # 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])
-
-        # Use iddfs alphabeta
         else:
-            move = IDDFS(
+            move, score = IDDFS(
-                self._board,
+                self._board, self.nnheuristic, self._mycolor, duration=duration, maxdepth=64
-                self.nnheuristic,
-                self._mycolor,
-                duration=duration,
-                max_depth=64,
             )
 
-        nd = time.time()
-        self.time += nd - st
-
-        print(move, (nd - st), file=stderr)
-
         self._board.push(move)
+        print(move, score, file=stderr)
+        nd = time.time()
+        self.time += (nd - st)
+
+        # New here: allows to consider internal representations of moves
+        # move is an internal representation. To communicate with the interface I need to change if to a string
         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 = Goban.Board.name_to_flat(move)
+        self.last_op_move = move
 
     def newGame(self, color):
-        self._board = Goban.Board()
         self._mycolor = color
-        self.last_op_move = -2
+        self._opponent = Goban.Board.flip(color)
-        self.time = 0
 
     def endGame(self, winner):
         if self._mycolor == winner: