def get_player_input():
# Die weißen figuren gehören zum Computer
print("Eingabe in Form von SpalteZeile SpalteZeile. Beispiel A7 A6")
cheesfield = ChessField()
cheesfield.draw_field()
minimax = mima.MiniMax(cheesfield)
need_input = True
while need_input:
raw_input = input('Please make your move ')
try:
coordinates = cheesfield.convert_input_to_coordinates(raw_input)
cheesfield.move_figure_to_position(coordinates[0], coordinates[1],
False)
except BaseException as e:
print(str(e))
continue
winner = cheesfield.get_winner()
if winner is None:
minimax.compute()
winner = cheesfield.get_winner()
cheesfield.draw_field()
if winner is not None:
output_winner(winner)
def __init__(self, board, **kwargs):
super(UCT, self).__init__(board, **kwargs)
self.stats = {}
self.max_depth = 0
self.data = {}
time = 30 # should be 1 min but in case that time is over
self.calculation_time = float(time)
# self.calculation_time = float(kwargs.get('time', 3)) # @ST @NOTE Here calculation_time should be 1 min
self.max_actions = int(kwargs.get('max_actions', 64))
# Exploration constant, increase for more exploratory actions,
# decrease to prefer actions with known higher win rates.
self.C = float(kwargs.get('C', 1.96)) #Original1.4
self.plugged_in_minimax = minimax.MiniMax(reversi.Board())
self.minimax_max_depth = 1
def main():
# g = game.SuperTicTacToe()
# g.start()
# g.play()
wins = collections.defaultdict(int)
numTrials = 100
print('out of ' + str(numTrials) + ' games....')
for i in range(0, numTrials):
# game = simulate.Simulate(agent1 = mcts.MonteCarloTreeSearch(g.SuperTicTacToe(verbose = 0)), agent2 = random_agent.RandomAgent(), game = g.SuperTicTacToe(verbose = 0), verbose = 0)
# result = game.run(trial)
game = simulate.Simulate(\
# agent1 = deep_q_learning.DeepQLearning(),\
agent1 = minimax.MiniMax(), \
agent2 = random_agent.RandomAgent(),\
game = g.SuperTicTacToe(verbose = 0),\
verbose = 1\
)
result = game.run(i)
wins[str(result)] += 1
print('x won ' + str(wins['x'] / float(numTrials)) + '% of the time')
print('o won ' + str(wins['o'] / float(numTrials)) + '% of the time')
print('tie ' + str(wins['False'] / float(numTrials)) + '% of the time')
game_status = get_game_status(game_board)
if game_status == 2:
print("BLUE WINS!")
if game_status == 3:
print("ITS A TIE!")
if game_status != 0:
print_char_board(alphabet_board)
return game_status
move_number += 1
if __name__ == "__main__":
alpha_beta_red = alphabeta.AlphaBeta(1, 3)
alpha_beta_blue = alphabeta.AlphaBeta(2, 3)
minimax_red = minimax.MiniMax(1, 3)
minimax_blue = minimax.MiniMax(2, 3)
reflex_red = reflex.ReflexAgent(1)
reflex_blue = reflex.ReflexAgent(2)
print("Alpha-Beta vs MiniMax")
start_1 = time.time()
play_game(alpha_beta_red, minimax_blue)
end_1 = time.time()
print("total runtime : {0:.3f} seconds".format(end_1 - start_1))
print("Red nodes expanded: " + str(alpha_beta_red.get_nodes_expanded()))
alpha_beta_red.reset()
print("Blue nodes expanded: " + str(minimax_blue.get_nodes_expanded()))
minimax_blue.reset()
print("MiniMax vs Alpha-Beta")
#Name: Anton Strickland
import minimax
import state
import visual
tryAgain = True
playing = True
aiTurn = True
gameboard = ['1', '2', '3', '4', '5', '6', '7', '8', '9']
validMoves = ['1', '2', '3', '4', '5', '6', '7', '8', '9']
actualState = minimax.State(gameboard[:])
game = minimax.MiniMax()
remainingMoves = validMoves[:]
Vis = visual.Visualizer()
winString = ""
while(tryAgain):
while(playing):
Vis.visualize(actualState,0,100)
if (actualState.aiTurn):
testState = minimax.State(actualState.grid[:])
# print(testState)
move = game.MiniMaxDecision(testState)
actualState.TakeTurn(move)
remainingMoves.remove(move)
else:
while(move not in remainingMoves):
move = Vis.getInput()
actualState.TakeTurn(int(move))
def main():
monte = montecarlo.MonteCarlo()
minim = minimax.MiniMax()
import player
import gameBoard
import random
import minimax
import time
from numpy import inf
if __name__ == '__main__':
# Setup Game
player1 = player.Player(0)
player2 = player.Player(1)
players = [player1, player2]
myGameBoard = gameBoard.GameBoard()
myGameBoard.printGameBoardWorkers(-1, -1, players)
myMiniMax = minimax.MiniMax()
# Initial worker placement
# for playerNum in range(2):
# for workerNum in range(2):
# placedWorker = False
# while placedWorker == False:
# row = random.randint(0, 4)
# col = random.randint(0, 4)
# placedWorker = myGameBoard.placeInitialWorker(players[playerNum], workerNum, row, col)
# myGameBoard.printGameBoardWorkers(-1, -1, players)
myGameBoard.placeInitialWorker(players[0], 0, 0, 0)
myGameBoard.placeInitialWorker(players[0], 1, 0, 1)
myGameBoard.placeInitialWorker(players[1], 0, 1, 0)
myGameBoard.placeInitialWorker(players[1], 1, 1, 1)
for turnNum in range(100):
print('TURN: ', turnNum)
import mancala
import minimax
m = mancala.Mancala("Human", "CPU")
ai = minimax.MiniMax(mancala.PLAYER2, depth=5)
while not m.is_game_over():
try:
print(m)
if m.current_player() == mancala.PLAYER1:
move = int(
input("current player: %s" % m.get_current_player_name()))
else:
print("getting move from AI")
move = ai.get_next_move(m)
print("AI chose %i" % move)
m.play_turn(move)
except Exception as e:
print(e.message)
print(m.get_winner() + " wins!")