tweaked mcts score evaluation: considered steps when scoring the board
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2
eval.py
2
eval.py
@@ -177,6 +177,6 @@ def score_stockfish(board: chess.Board) -> chess.engine.PovScore:
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:return:
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"""
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engine = chess.engine.SimpleEngine.popen_uci("./stockfish/stockfish-ubuntu-x86-64-avx2")
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info = engine.analyse(board, chess.engine.Limit(depth=20))
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info = engine.analyse(board, chess.engine.Limit(depth=2))
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engine.quit()
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return info["score"]
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8
main.py
8
main.py
@@ -5,7 +5,7 @@ import engine
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import eval
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def test_mcts(seed):
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def test_mcts():
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fools_mate = "rnbqkbnr/pppp1ppp/4p3/8/5PP1/8/PPPPP2P/RNBQKBNR b KQkq f3 0 2"
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board = chess.Board(fools_mate)
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mcts_root = MCTSNode(board)
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@@ -15,7 +15,7 @@ def test_mcts(seed):
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print("move (mcts):", c.move, " with score:", c.score)
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def test_stockfish(seed):
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def test_stockfish():
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fools_mate = "rnbqkbnr/pppp1ppp/4p3/8/5PP1/8/PPPPP2P/RNBQKBNR b KQkq f3 0 2"
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board = chess.Board(fools_mate)
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moves = {}
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@@ -37,8 +37,8 @@ def analyze_results(moves: dict):
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def main():
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test_mcts(0)
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test_stockfish(0)
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test_mcts()
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test_stockfish()
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if __name__ == '__main__':
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6
mcts.py
6
mcts.py
@@ -30,21 +30,23 @@ class MCTSNode:
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self.children.append(child_node)
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return child_node
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def _rollout(self, rollout_depth: int = 100) -> float:
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def _rollout(self, rollout_depth: int = 20) -> int:
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"""
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Rolls out the node by simulating a game for a given depth.
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Sometimes this step is called 'simulation' or 'playout'.
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:return: the score of the rolled out game
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"""
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copied_board = self.board.copy()
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steps = 1
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for i in range(rollout_depth):
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if copied_board.is_game_over():
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break
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m = engine.pick_move(copied_board)
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copied_board.push(m)
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steps += 1
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return eval.score_manual(copied_board)
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return eval.score_manual(copied_board) // steps
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def _backpropagate(self, score: float) -> None:
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"""
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