masouduut94

def search(self, time_budget: int) -> None:
    """
    Search and update the search tree for a
    specified amount of time in seconds.

    """
    start_time = clock()
    num_rollouts = 0

    # do until we exceed our time budget

masouduut94

class UctMctsAgent:
    """
    Basic no frills implementation of an agent that preforms MCTS for hex.
    Attributes:
        root_state (GameState): Game simulator that helps us to understand the game situation
        root (Node): Root of the tree search
        run_time (int): time per each run
        node_count (int): the whole nodes in tree
        num_rollouts (int): The number of rollouts for each search
        

masouduut94

from tkinter import (Frame, Canvas, ttk, HORIZONTAL, VERTICAL, IntVar, Scale, Button, Label, PhotoImage, BOTH, LEFT, Y,
                     X, TOP, messagebox)

from numpy import int_

from gamestate import GameState
from meta import GameMeta
from uct_mcstsagent import UctMctsAgent

masouduut94

class MCTSMeta:
    EXPLORATION = 0.5
    RAVE_CONST = 300
    RANDOMNESS = 0.5
    POOLRAVE_CAPACITY = 10
    K_CONST = 10
    A_CONST = 0.25
    WARMUP_ROLLOUTS = 7

masouduut94

from tkinter import Tk

from gui import Gui


def main():
    root = Tk()
    interface = Gui(root)
    root.mainloop()

masouduut94

from math import sqrt, log
from copy import deepcopy
from random import choice, random
from time import clock

from gamestate import GameState
from uct_mcstsagent import Node, UctMctsAgent
from meta import *

masouduut94

class RaveMctsAgent(UctMctsAgent):
    def __init__(self, state: GameState = GameState(8)):
        self.root_state = deepcopy(state)
        self.root = RaveNode() # Replace Node with RaveNode
        self.run_time = 0
        self.node_count = 0
        self.num_rollouts = 0

    def set_gamestate(self, state: GameState) -> None:
        """

masouduut94

from tkinter import (Frame, Canvas, ttk, HORIZONTAL, VERTICAL, IntVar, Scale, Button, Label, PhotoImage, BOTH, LEFT, Y,
                     X, TOP, messagebox)

from numpy import int_

from gamestate import GameState
from meta import GameMeta
from rave_mctsagent import (RaveMctsAgent, LGRMctsAgent, PoolRaveMctsAgent, DecisiveMoveMctsAgent)
from ucb1_tuned_mctsagent import UCB1TunedMctsAgent
from uct_mcstsagent import UctMctsAgent

masouduut94

def would_lose(self, cell: tuple, color: int) -> bool:
    """
    Return True is the move indicated by cell and color would lose the game,
    False otherwise.
    """
    connect1 = False
    connect2 = False
    if color == GameMeta.PLAYERS['black']:
        if cell[1] == 0:
            connect1 = True

masouduut94

class DecisiveMoveMctsAgent(RaveMctsAgent):
    def roll_out(self, state: GameState) -> tuple:
        """
        Simulate a random game except that we play all known critical cells
        first, return the winning player and record critical cells at the end.
        """
        moves = state.moves()
        good_moves = moves.copy()
        good_opponent_moves = moves.copy()
        to_play = state.turn()