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added reuse of subtree for simulations (apply_move), played around with rollout depth

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Lukas Kuess
2024-01-29 12:26:19 +01:00
parent a2cb3a5719
commit f1a36964df
7 changed files with 80 additions and 42 deletions
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25
chesspp/baysian_mcts.py
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@@ -1,17 +1,15 @@
import chess
from chesspp.i_mcts import *
from chesspp.i_strategy import IStrategy
from chesspp.util_gaussian import gaussian_ucb1, max_gaussian, min_gaussian
from chesspp.eval import score_manual
import numpy as np
import math
class BayesianMctsNode(IMctsNode):
def __init__(self, board: chess.Board, strategy: IStrategy, color: chess.Color, parent: Self | None, move: chess.Move | None,
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):
super().__init__(board, strategy, parent, move, random_state)
self.color = color # Color of the player whose turn it is
self.color = color # Color of the player whose turn it is
self.visits = 0
self.result = inherit_result if inherit_result is not None else 0
self._set_mu_sigma()
@@ -20,7 +18,8 @@ class BayesianMctsNode(IMctsNode):
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)
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
@@ -74,7 +73,7 @@ class BayesianMctsNode(IMctsNode):
return self._select_best_child()
def rollout(self, rollout_depth: int = 20) -> int:
def rollout(self, rollout_depth: int = 4) -> int:
copied_board = self.board.copy()
steps = self.depth
for i in range(rollout_depth):
@@ -124,29 +123,29 @@ class BayesianMctsNode(IMctsNode):
self.parent.backpropagate()
def print(self, indent=0):
print("\t"*indent + f"move={self.move}, visits={self.visits}, mu={self.mu}, sigma={self.sigma}")
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)
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)
self.root = BayesianMctsNode(board, strategy, color, None, None, self.random_state)
self.root.visits += 1
self.color = color
def sample(self, runs: int = 1000) -> None:
for i in range(runs):
#print(f"sample {i}")
# print(f"sample {i}")
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 = not self.color
self.color = self.board.turn
# if a child node contains the move, set this child as new root
for child in self.get_children():
@@ -169,4 +168,4 @@ class BayesianMcts(IMcts):
def print(self):
print("================================")
self.root.print()
self.root.print()