Merge branch 'mcts' into 'main'

Mcts

See merge request tu-wien/prob-prog!1

.gitignore

@@ -1,2 +1,3 @@
 /stockfish/
-.idea
+.idea
+.venv

engine.py

@@ -2,30 +2,8 @@ import chess
 import chess.engine
 import random
 import eval
-
+import numpy as np
-
+from stockfish import Stockfish
-def main():
-    fools_mate = "rnbqkbnr/pppp1ppp/4p3/8/5PP1/8/PPPPP2P/RNBQKBNR b KQkq f3 0 2"
-    board = chess.Board(fools_mate)
-    print(board, '\n')
-    moves = {}
-    for i in range(10):
-        move = pick_move(board)
-        if move is None:
-            break
-
-        simulate_game(board, move, 100)
-        moves[move] = board
-        board = chess.Board(fools_mate)
-
-    analyze_results(moves)
-
-
-def analyze_results(moves: dict):
-    for m, b in moves.items():
-        manual_score = eval.score_game(b)
-        engine_score = eval.analyze_with_stockfish(b)
-        print(f"score for move {m}: manual_score={manual_score}, engine_score={engine_score}")
 
 
 def pick_move(board: chess.Board) -> chess.Move | None:
@@ -49,19 +27,54 @@ def simulate_game(board: chess.Board, move: chess.Move, depth: int):
     """
     engine = chess.engine.SimpleEngine.popen_uci("./stockfish/stockfish-ubuntu-x86-64-avx2")
     board.push(move)
-    print(move)
-    print(board, '\n')
     for i in range(depth):
         if board.is_game_over():
             engine.quit()
             return
         r = engine.play(board, chess.engine.Limit(depth=2))
-        print(r)
         board.push(r.move)
-        print(board, '\n')
 
     engine.quit()
 
 
-if __name__ == '__main__':
+def simulate_stockfish_prob(board: chess.Board, move: chess.Move, games: int = 10, depth: int = 10) -> (float, float):
-    main()
+    """
+    Simulate a game using
+    :param board: chess board
+    :param move: chosen move
+    :param games: number of games that should be simulated after playing the move
+    :param depth: simulation depth per game
+    :return:
+    """
+    board.push(move)
+    copied_board = board.copy()
+    scores = []
+
+    stockfish = Stockfish("./stockfish/stockfish-ubuntu-x86-64-avx2", depth=2, parameters={"Threads": 8, "Hash": 2048})
+    stockfish.set_elo_rating(1200)
+    stockfish.set_fen_position(board.fen())
+
+    def reset_game():
+        nonlocal scores, copied_board, board
+        score = eval.score_stockfish(copied_board).white().score(mate_score=100_000)
+        scores.append(score)
+        copied_board = board.copy()
+        stockfish.set_fen_position(board.fen())
+
+    for _ in range(games):
+        for d in range(depth):
+            if copied_board.is_game_over() or d == depth - 1:
+                reset_game()
+                break
+
+            if d == depth - 1:
+                reset_game()
+
+            top_moves = stockfish.get_top_moves(3)
+            chosen_move = random.choice(top_moves)['Move']
+            stockfish.make_moves_from_current_position([chosen_move])
+            copied_board.push(chess.Move.from_uci(chosen_move))
+
+    print(scores)
+    # TODO: return distribution here?
+    return np.array(scores).mean(), np.array(scores).std()

eval.py

@@ -1,5 +1,6 @@
 import chess
 import chess.engine
+import sys
 
 # Eval constants for scoring chess boards
 # Evaluation metric inspired by Tomasz Michniewski: https://www.chessprogramming.org/Simplified_Evaluation_Function
@@ -136,9 +137,7 @@ def check_endgame(board: chess.Board) -> bool:
     return (queens_black == 0 and queens_white == 0) or ((queens_black >= 1 and minors_black <= 1) or (queens_white >= 1 and minors_white <= 1))
 
 
-
+def score_manual(board: chess.Board) -> int:
-
-def score_game(board: chess.Board) -> float:
     """
     Calculate the score of the given board regarding the given color
     :param board: the chess board
@@ -147,7 +146,7 @@ def score_game(board: chess.Board) -> float:
     outcome = board.outcome()
     if outcome is not None:
         if outcome.termination == chess.Termination.CHECKMATE:
-            return float('inf') if outcome.winner == chess.WHITE else float('-inf')
+            return sys.maxsize if outcome.winner == chess.WHITE else -sys.maxsize
         else:  # draw
             return 0
 
@@ -171,13 +170,13 @@ def score_game(board: chess.Board) -> float:
     return score
 
 
-def analyze_with_stockfish(board: chess.Board) -> chess.engine.PovScore:
+def score_stockfish(board: chess.Board) -> chess.engine.PovScore:
     """
     Calculate the score of the given board using stockfish
     :param board:
     :return:
     """
     engine = chess.engine.SimpleEngine.popen_uci("./stockfish/stockfish-ubuntu-x86-64-avx2")
-    info = engine.analyse(board, chess.engine.Limit(depth=20))
+    info = engine.analyse(board, chess.engine.Limit(depth=2))
     engine.quit()
     return info["score"]

main.py

@@ -0,0 +1,61 @@
+import chess
+import chess.engine
+from mcts import MCTSNode
+import engine
+import eval
+
+
+def test_mcts():
+    fools_mate = "rnbqkbnr/pppp1ppp/4p3/8/5PP1/8/PPPPP2P/RNBQKBNR b KQkq f3 0 2"
+    board = chess.Board(fools_mate)
+    mcts_root = MCTSNode(board)
+    mcts_root.build_tree()
+    sorted_moves = sorted(mcts_root.children, key=lambda x: x.move.uci())
+    for c in sorted_moves:
+        print("move (mcts):", c.move, " with score:", c.score)
+
+
+def test_stockfish():
+    fools_mate = "rnbqkbnr/pppp1ppp/4p3/8/5PP1/8/PPPPP2P/RNBQKBNR b KQkq f3 0 2"
+    board = chess.Board(fools_mate)
+    moves = {}
+    untried_moves = list(board.legal_moves)
+    for move in untried_moves:
+        engine.simulate_game(board, move, 100)
+        moves[move] = board
+        board = chess.Board(fools_mate)
+
+    sorted_moves = dict(sorted(moves.items(), key=lambda x: x[0].uci()))
+    analyze_results(sorted_moves)
+
+
+def test_stockfish_prob():
+    fools_mate = "rnbqkbnr/pppp1ppp/4p3/8/5PP1/8/PPPPP2P/RNBQKBNR b KQkq f3 0 2"
+    board = chess.Board(fools_mate)
+    moves = {}
+    untried_moves = list(board.legal_moves)
+    for move in untried_moves:
+        mean, std = engine.simulate_stockfish_prob(board, move, 10, 4)
+        moves[move] = (mean, std)
+        board = chess.Board(fools_mate)
+
+    sorted_moves = dict(sorted(moves.items(), key=lambda x: x[0].uci()))
+    for m, s in sorted_moves.items():
+        print(f"move '{m.uci()}' (prob_stockfish): mean={s[0]}, std={s[1]}")
+
+
+def analyze_results(moves: dict):
+    for m, b in moves.items():
+        manual_score = eval.score_manual(b)
+        engine_score = eval.score_stockfish(b).white()
+        print(f"score for move {m}: manual_score={manual_score}, engine_score={engine_score}")
+
+
+def main():
+    test_mcts()
+    test_stockfish()
+    test_stockfish_prob()
+
+
+if __name__ == '__main__':
+    main()

mcts.py

@@ -0,0 +1,105 @@
+import chess
+import random
+import eval
+import engine
+import numpy as np
+
+
+class MCTSNode:
+    def __init__(self, board: chess.Board, parent = None, move: chess.Move | None = None, random_state: int | None = None):
+        self.random = random.Random(random_state)
+        self.board = board
+        self.parent = parent
+        self.move = move
+        self.children = []
+        self.visits = 0
+        self.legal_moves = list(board.legal_moves)
+        self.untried_actions = self.legal_moves
+        self.score = 0
+
+    def _expand(self) -> 'MCTSNode':
+        """
+        Expands the node, i.e., choose an action and apply it to the board
+        :return:
+        """
+        move = self.random.choice(self.untried_actions)
+        self.untried_actions.remove(move)
+        next_board = self.board.copy()
+        next_board.push(move)
+        child_node = MCTSNode(next_board, parent=self, move=move)
+        self.children.append(child_node)
+        return child_node
+
+    def _rollout(self, rollout_depth: int = 20) -> int:
+        """
+        Rolls out the node by simulating a game for a given depth.
+        Sometimes this step is called 'simulation' or 'playout'.
+        :return: the score of the rolled out game
+        """
+        copied_board = self.board.copy()
+        steps = 1
+        for i in range(rollout_depth):
+            if copied_board.is_game_over():
+                break
+
+            m = engine.pick_move(copied_board)
+            copied_board.push(m)
+            steps += 1
+
+        return eval.score_manual(copied_board) // steps
+
+    def _backpropagate(self, score: float) -> None:
+        """
+        Backpropagates the results of the rollout
+        :param score:
+        :return:
+        """
+        self.visits += 1
+        # TODO: maybe use score + num of moves together (a win in 1 move is better than a win in 20 moves)
+        self.score += score
+        if self.parent:
+            self.parent._backpropagate(score)
+
+    def is_fully_expanded(self) -> bool:
+        return len(self.untried_actions) == 0
+
+    def _best_child(self) -> 'MCTSNode':
+        """
+        Picks the best child according to our policy
+        :return: the best child
+        """
+        # NOTE: maybe clamp the score between [-1, +1] instead of [-inf, +inf]
+        choices_weights = [(c.score / c.visits) + np.sqrt(((2 * np.log(self.visits)) / c.visits))
+                           for c in self.children]
+        return self.children[np.argmax(choices_weights)]
+
+    def _select_leaf(self) -> 'MCTSNode':
+        """
+        Selects a leaf node.
+        If the node is not expanded is will be expanded.
+        :return: Leaf node
+        """
+        current_node = self
+        while not current_node.board.is_game_over():
+            if not current_node.is_fully_expanded():
+                return current_node._expand()
+            else:
+                current_node = current_node._best_child()
+
+        return current_node
+
+    def build_tree(self, samples: int = 1000) -> 'MCTSNode':
+        """
+        Runs the MCTS with the given number of samples
+        :param samples: number of simulations
+        :return: best node containing the best move
+        """
+        for i in range(samples):
+            # selection & expansion
+            # rollout
+            # backpropagate score
+            node = self._select_leaf()
+            score = node._rollout()
+            node._backpropagate(score)
+
+        return self._best_child()

requirements.txt

@@ -1 +1,3 @@
-chess==1.10.0
+chess==1.10.0
+numpy==1.26.3
+stockfish==3.28.0