feat: fix indent

go_player/.gitignore

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

go_player/Makefile

@@ -0,0 +1,27 @@
+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

@@ -0,0 +1,31 @@
+# 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

@@ -2,7 +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

@@ -5,83 +5,74 @@ 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)
-    if depth == 0 or board.is_game_over():
-        return heuristic(board, board.next_player()), move
+    wantMax = (board.next_player() == color)
+    best_move = -1
 
     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 = (
-                _alphabeta(
-                    board,
-                    alpha=alpha,
-                    beta=beta,
-                    move=move,
-                    heuristic=heuristic,
-                    color=color,
-                    depth=depth - 1,
-                )[0],
-                move,
-            )
-            acc = max(
-                acc,
-                value,
-                key=lambda t: t[0],
-            )
+            value = _alphabeta(
+                board,
+                heuristic=heuristic,
+                color=color,
+                alpha=alpha,
+                beta=beta,
+                depth=depth - 1,
+                shouldStop=shouldStop,
+            )[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 = (
-                _alphabeta(
-                    board,
-                    alpha=alpha,
-                    beta=beta,
-                    move=move,
-                    heuristic=heuristic,
-                    color=color,
-                    depth=depth - 1,
-                )[0],
-                move,
-            )
-            acc = min(
-                acc,
-                value,
-                key=lambda t: t[0],
-            )
+            value = _alphabeta(
+                board,
+                heuristic=heuristic,
+                color=color,
+                alpha=alpha,
+                beta=beta,
+                depth=depth - 1,
+                shouldStop=shouldStop,
+            )[0]
             board.pop()
 
-            if shouldStop() or acc[0] <= alpha:
-                break  # alpha cutoff
-            beta = min(beta, acc[0])
+            if value < acc:
+                acc = value
+                best_move = move
 
-    return acc
+            beta = min(beta, acc)
+            if shouldStop() or acc <= alpha:
+                break  # alpha cutoff
+
+    return acc, best_move
 
 
 def alphabeta(
@@ -90,36 +81,30 @@ 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):
-    st = time.time()
-    shouldStop = (lambda: time.time() - st > duration)
-    depth = 0
-    move = -1
-    score = -1
+def IDDFS(
+    board: Goban.Board,
+    heuristic: Callable[[Goban.Board, Any], float],
+    color,
+    max_depth: int = 10,
+    duration: float = 5.0,  # Duration in seconds
+):
+    best_move = -1
+    start_time = time.time()
+    shouldStop = lambda: (time.time() - start_time) >= duration
 
-    while not shouldStop() and depth <= maxdepth:
-        if depth % 2 == 0:
-            score, move = _alphabeta(
-                    board, heuristic, color, move=move, alpha=-math.inf, beta=math.inf, depth=depth, shouldStop=shouldStop
-            )
+    for depth in range(1, max_depth + 1):
+        value, move = _alphabeta(
+            board, heuristic=heuristic, color=color, depth=depth, shouldStop=shouldStop
+        )
 
-            if score == math.inf:
-                return move, score
+        if shouldStop():
+            break
 
-        else:
-            score, move = _alphabeta(
-                board, heuristic, color, move=move, alpha=-math.inf, beta=math.inf, depth=depth, shouldStop=shouldStop
-            )
+        print(f"{depth}, {value}", file=stderr)
+        best_move = 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
+    return best_move

go_player/myPlayer.py

@@ -9,14 +9,14 @@ from sys import stderr
 import time
 import math
 import Goban
-from random import choice
-from moveSearch import IDDFS, alphabeta
+from moveSearch import IDDFS
 from playerInterface import *
 import torch
 import torch.nn as nn
 import torch.nn.functional as F
 import numpy as np
-from torch.utils.data import Dataset
+import json
+
 
 def setup_device():
     # Allows to use the GPU if available
@@ -31,8 +31,11 @@ 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):
@@ -42,9 +45,10 @@ 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
 
-    return goBoard
+    # sometime, a little bit of magic is required
+    return torch.from_numpy(np.array([goBoard])).float()
 
 
 class GoModel(nn.Module):
@@ -52,50 +56,44 @@ class GoModel(nn.Module):
         super(GoModel, self).__init__()
 
         self.net = torch.nn.Sequential(
-             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.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(),
         )
 
     def forward(self, x):
-        if self.training: 
+        if self.training:
             return self.net(x)
         else:
             y = self.net(x)
             batch_size = x.size(0)
 
-            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]
+            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]
             x_rotated = x_rotated.view(-1, 3, 8, 8)  # [batch_size*4, 3, 8, 8]
 
             with torch.no_grad():
@@ -103,7 +101,7 @@ class GoModel(nn.Module):
 
             # Reshape to get them by rotation
             y_rotated = y_rotated.view(batch_size, 4, -1)  # [batch_size, 4, 1]
-            y_mean = y_rotated.mean(dim=1)       # [batch_size, 1]
+            y_mean = y_rotated.mean(dim=1)  # [batch_size, 1]
 
             return y_mean
 
@@ -118,38 +116,44 @@ 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() == 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)
+        go_board = goban2Go(board).to(self.device)
 
         self.model.eval()
         with torch.no_grad():
@@ -166,47 +170,68 @@ 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 = 3
-
-        elif self._board._nbBLACK + self._board._nbWHITE < 30:
             duration = 5
 
-        elif self._board._nbBLACK + self._board._nbWHITE > 40:
-            duration = 64 - (self._board._nbBLACK + self._board._nbWHITE)
-
-        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
-            score = math.inf
+        elif self._board._nbBLACK + self._board._nbWHITE < 40:
+            duration = 20
 
         else:
-            move, score = IDDFS(
-                self._board, self.nnheuristic, self._mycolor, duration=duration, maxdepth=64
+            duration = 30
+
+        # Play quickly if lack of time
+        duration = min(duration, (self.maxtime - self.time) / 10)
+
+        move = -1
+        b, w = self._board.compute_score()
+
+        # 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(
+                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.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
+        print(move, (nd - st), file=stderr)
+
+        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)
 
     def newGame(self, color):
+        self._board = Goban.Board()
         self._mycolor = color
-        self._opponent = Goban.Board.flip(color)
+        self.last_op_move = -2
+        self.time = 0
 
     def endGame(self, winner):
         if self._mycolor == winner: