Added basic bayes-mcts using beta distribution

src/chesspp/baysian_mcts.py

@@ -0,0 +1,145 @@
+import chess
+from src.chesspp.i_mcts import *
+from src.chesspp.i_strategy import IStrategy
+from src.chesspp.util_gaussian import gaussian_ucb1, max_gaussian, beta_std, beta_mean
+from src.chesspp.eval import *
+import numpy as np
+import math
+
+
+class BayesianMctsNode(IMctsNode):
+    def __init__(self, board: chess.Board, strategy: IStrategy, parent: Self | None, move: chess.Move | None,
+                 random_state: random.Random, inherit_results: list[int] | None = None):
+        super().__init__(board, strategy, parent, move, random_state)
+        self.visits = 0
+        self.results = inherit_results.copy() if inherit_results is not None else [1, 1]
+
+        self._set_mu_sigma()
+
+    def _create_child(self, move: chess.Move):
+        copied_board = self.board.copy()
+        copied_board.push(move)
+        return BayesianMctsNode(copied_board, self.strategy, self, move, self.random_state, inherit_results=self.results)
+
+    def _set_mu_sigma(self):
+        alpha = self.results[0]
+        beta = self.results[1]
+
+        self.mu = beta_mean(alpha, beta)
+        self.sigma = beta_std(alpha, beta)
+
+    def _select_child(self) -> IMctsNode:
+        # select child by modified UCB1
+        if self.board.is_game_over():
+            return self
+
+        best_child = self.random_state.choice(self.children)
+        best_val = gaussian_ucb1(best_child.mu, best_child.sigma, self.visits)
+        for c in self.children:
+            g = gaussian_ucb1(c.mu, c.sigma, self.visits)
+
+            if g > best_val:
+                best_val = g
+                best_child = c
+        return best_child
+
+    def select(self) -> IMctsNode:
+        if len(self.children) == 0:
+            return self
+        else:
+            return self._select_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_child()
+
+    def rollout(self, rollout_depth: int = 20) -> int:
+        copied_board = self.board.copy()
+        steps = 1
+        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
+
+        score = eval.score_manual(copied_board) // steps
+        if score > 0:
+            self.results[1] += 1
+        else:
+            self.results[0] += abs(score) // 50_000
+        return score
+
+    def backpropagate(self, score: int | None = None) -> None:
+        self.visits += 1
+
+        if score is not None:
+            self.results.append(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))
+            max_mu = shuffled_children[0].mu
+            max_sigma = shuffled_children[0].sigma
+            for c in shuffled_children[1:]:
+                max_mu, max_sigma = max_gaussian(max_mu, max_sigma, c.mu, c.sigma)
+
+            if max_sigma == 0:
+                max_sigma = 0.001
+            self.mu = max_mu
+            self.sigma = max_sigma
+
+        if self.parent:
+            self.parent.backpropagate()
+
+    def print(self, indent=0):
+        print("\t"*indent + f"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, seed: int | None = None):
+        super().__init__(board, strategy, seed)
+        self.root = BayesianMctsNode(board, strategy, None, None, self.random_state)
+        self.root.visits += 1
+
+    def sample(self, runs: int = 1000) -> None:
+        for i in range(runs):
+            #print(f"sample {i}")
+            leaf_node = self.root.select().expand()
+            _ = leaf_node.rollout()
+            leaf_node.backpropagate()
+            #self.root.print()
+
+    def apply_move(self, move: chess.Move) -> None:
+        self.board.push(move)
+
+        # 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
+                self.root.parent = None
+                return
+
+        # if no child node contains the move, initialize a new tree.
+        self.root = BayesianMctsNode(self.board, self.root.strategy, None, None, self.random_state)
+
+    def get_children(self) -> list[IMctsNode]:
+        return self.root.children
+
+    def print(self):
+        print("================================")
+        self.root.print()