Create folder mcts for all mcts related files

chesspp/mcts/baysian_mcts.py

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+import math
+
+import torch.distributions as dist
+from chesspp.mcts.i_mcts import *
+from chesspp.i_strategy import IStrategy
+from chesspp.util_gaussian import gaussian_ucb1, max_gaussian, min_gaussian
+
+
+class BayesianMctsNode(IMctsNode):
+    def __init__(self, board: chess.Board, strategy: IStrategy, color: chess.Color, parent: Self | None,
+                 move: chess.Move | None,
+                 random_state: random.Random, inherit_result: int | None = None, depth: int = 0, visits: int = 0):
+        super().__init__(board, strategy, parent, move, random_state)
+        self.color = color  # Color of the player whose turn it is
+        self.visits = visits
+        self.result = inherit_result if inherit_result is not None else 0
+        self._set_mu_sigma()
+        self.depth = depth
+
+    def _create_child(self, move: chess.Move) -> IMctsNode:
+        copied_board = self.board.copy()
+        copied_board.push(move)
+        return BayesianMctsNode(copied_board, self.strategy, not self.color, self, move, self.random_state, self.result,
+                                self.depth + 1)
+
+    def _set_mu_sigma(self) -> None:
+        self.mu = self.result
+        self.sigma = 1
+
+    def _is_new_ucb1_better(self, current, new) -> bool:
+        if self.color == chess.WHITE:
+            # maximize ucb1
+            return new > current
+        else:
+            # minimize ubc1
+            return new < current
+
+    def _select_best_child(self) -> IMctsNode:
+        """
+        Returns the child with the *best* ucb1 score.
+        It chooses the child with maximum ucb1 for WHITE, and with minimum ucb1 for BLACK.
+        """
+
+        if self.board.is_game_over():
+            return self
+
+        best_child = self.random_state.choice(self.children)
+        best_ucb1 = gaussian_ucb1(best_child.mu, best_child.sigma, self.visits)
+        for child in self.children:
+            # if child has no visits, prioritize this child.
+            if child.visits == 0:
+                best_child = child
+                break
+
+            # save child if it has a *better* score, than our previous best child.
+            ucb1 = gaussian_ucb1(child.mu, child.sigma, self.visits)
+            if self._is_new_ucb1_better(best_ucb1, ucb1):
+                best_ucb1 = ucb1
+                best_child = child
+
+        return best_child
+
+    def update_depth(self, depth: int) -> None:
+        self.depth = depth
+        for c in self.children:
+            c.update_depth(depth + 1)
+
+    def select(self) -> IMctsNode:
+        if len(self.children) == 0 or self.board.is_game_over():
+            return self
+
+        return self._select_best_child().select()
+
+    def expand(self) -> IMctsNode:
+        if self.visits == 0:
+            return self
+
+        for move in self.legal_moves:
+            self.children.append(self._create_child(move))
+
+        return self._select_best_child()
+
+    def rollout(self, rollout_depth: int = 4) -> int:
+        copied_board = self.board.copy()
+        steps = self.depth
+        for i in range(rollout_depth):
+            if copied_board.is_game_over():
+                break
+
+            m = self.strategy.pick_next_move(copied_board)
+            if m is None:
+                break
+
+            copied_board.push(m)
+            steps += 1
+
+        steps = max(1, steps)
+        score = int(self.strategy.analyze_board(copied_board) / (math.log2(steps) + 1))
+        self.result = score
+        return score
+
+    def _combine_gaussians(self, mu1: float, sigma1: float, mu2: float, sigma2: float) -> tuple[float, float]:
+        if self.color == chess.WHITE:
+            return max_gaussian(mu1, sigma1, mu2, sigma2)
+        else:
+            return min_gaussian(mu1, sigma1, mu2, sigma2)
+
+    def backpropagate(self, score: int | None = None) -> None:
+        self.visits += 1
+
+        if score is not None:
+            self.result = score
+
+        if len(self.children) == 0:
+            # leaf node
+            self._set_mu_sigma()
+        else:
+            # interior node
+            shuffled_children = self.random_state.sample(self.children, len(self.children))
+            mu = shuffled_children[0].mu
+            sigma = shuffled_children[0].sigma
+            for c in shuffled_children[1:]:
+                mu, sigma = self._combine_gaussians(mu, sigma, c.mu, c.sigma)
+
+            # if max_sigma == 0:
+            #     max_sigma = 0.001
+            self.mu = mu
+            self.sigma = sigma
+
+        if self.parent:
+            self.parent.backpropagate()
+
+    def print(self, indent=0):
+        print("\t" * indent + f"move={self.move}, visits={self.visits}, mu={self.mu}, sigma={self.sigma}")
+        for c in self.children:
+            c.print(indent + 1)
+
+
+class BayesianMcts(IMcts):
+
+    def __init__(self, board: chess.Board, strategy: IStrategy, color: chess.Color, seed: int | None = None):
+        super().__init__(board, strategy, seed)
+        self.root = BayesianMctsNode(board, strategy, color, None, None, self.random_state, visits=1)
+        self.color = color
+
+    def sample(self, runs: int = 1000) -> None:
+        for i in range(runs):
+            if self.board.is_game_over():
+                break
+
+            leaf_node = self.root.select().expand()
+            _ = leaf_node.rollout()
+            leaf_node.backpropagate()
+
+    def apply_move(self, move: chess.Move) -> None:
+        self.board.push(move)
+        self.color = self.board.turn
+
+        # if a child node contains the move, set this child as new root
+        for child in self.get_children():
+            if child.move == move:
+                self.root = child
+                child.depth = 0
+                self.root.parent = None
+                self.root.update_depth(0)
+                self.root.visits = 1
+                return
+
+        # if no child node contains the move, initialize a new tree.
+        self.root = BayesianMctsNode(self.board, self.root.strategy, self.color, None, None, self.random_state, visits=1)
+
+    def get_children(self) -> list[IMctsNode]:
+        return self.root.children
+
+    def get_moves(self) -> Dict[chess.Move, dist.Normal]:
+        res = {}
+        for c in self.root.children:
+            res[c.move] = dist.Normal(c.mu, c.sigma)
+        return res
+
+    def print(self):
+        print("================================")
+        self.root.print()