107 lines
2.8 KiB
Python
107 lines
2.8 KiB
Python
import chess
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import random
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from abc import ABC, abstractmethod
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from typing import Dict, Self
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from chesspp.i_strategy import IStrategy
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class IMctsNode(ABC):
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def __init__(self, board: chess.Board, strategy: IStrategy, parent: Self | None, move: chess.Move | None,
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random_state: random.Random):
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self.board = board
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self.strategy = strategy
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self.parent = parent
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self.children = []
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self.move = move
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self.legal_moves = list(board.legal_moves)
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self.random_state = random_state
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self.depth = 0
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@abstractmethod
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def select(self) -> Self:
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"""
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Selects the next node leaf node in the tree
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:return:
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"""
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pass
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@abstractmethod
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def expand(self) -> Self:
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"""
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Expands this node creating X child leaf nodes, i.e., choose an action and apply it to the board
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:return:
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"""
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pass
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@abstractmethod
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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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pass
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@abstractmethod
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def backpropagate(self, score: float) -> None:
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"""
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Backpropagates the results of the rollout
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:param score:
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:return:
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"""
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pass
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def update_depth(self, depth: int) -> None:
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"""
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Recursively updates the depth the current node and all it's children
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:param depth: new depth for current node
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:return:
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"""
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class IMcts(ABC):
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def __init__(self, board: chess.Board, strategy: IStrategy, seed: int | None):
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self.board = board
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self.strategy = strategy
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self.random_state = random.Random(seed)
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@abstractmethod
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def sample(self, runs: int = 1000) -> None:
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"""
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Run the MCTS simulation
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:param runs: number of runs
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:return:
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"""
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pass
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@abstractmethod
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def apply_move(self, move: chess.Move) -> None:
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"""
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Apply the move to the chess board
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:param move: move to apply
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:return:
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"""
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pass
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@abstractmethod
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def get_children(self) -> list[IMctsNode]:
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"""
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Return the immediate children of the root node
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:return: list of immediate children of mcts root
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"""
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pass
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@abstractmethod
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def get_moves(self) -> Dict[chess.Move, int]:
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"""
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Return all legal moves from this node with respective scores
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:return: dictionary with moves as key and scores as values
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"""
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pass
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"""
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TODO: add score class:
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how many moves until the end of the game?
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score ranges?
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perspective of white/black
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""" |