def playGame(self):
# sets up first two rounds
self.setUp()
winner = None
count = 0
while (True):
count += 1
if count > 100:
break
for player in self.players:
roll = random.randint(1, 6)
roll2 = random.randint(1, 6)
sum = roll + roll2
for vertex in self.built_vertices:
for tile in vertex.tiles:
if tile.probability == sum:
for i in range(0, vertex.settlementType):
vertex.player.drawResourceCard(tile.type)
while (True):
mcts = mcts(timeLimit=1000)
action = mcts.search(self)
if action is None:
break
else:
self.takeAction(action)
if player.victoryPoints >= 10:
winner = player
break
return winner
def main():
# generate_random_graph(50, 0.08)
state = CliqueState()
while not state.isTerminal():
best_action = mcts.search(initialState=state)
state = state.takeAction(best_action)
draw_graph(G, state.clique_set)
print(state)
def mctree(mcts, state):
curState = state
numMoves = 0
visited = []
while (True):
visited.append(curState)
if curState.isTerminal():
break
action = mcts.search(initialState=curState)
curState = curState.takeAction(action)
mcts.add(curState)
numMoves += 1
return numMoves
for row in self.board:
s += " ".join([{0: "#", 1: "X", -1: "O"}[x] for x in row]) + "\n"
return s
class Action():
def __init__(self, player, x, y):
self.player = player
self.x = x
self.y = y
def __str__(self):
return str((self.x, self.y))
def __repr__(self):
return str(self)
def __eq__(self, other):
return self.__class__ == other.__class__ and self.x == other.x and \
self.y == other.y and self.player == other.player
def __hash__(self):
return hash((self.x, self.y, self.player))
if __name__ == "__main__":
from mcts import mcts
initialState = NaughtsAndCrossesState()
mcts = mcts(iterationLimit=10000)
action = mcts.search(initialState=initialState)
elif counter > 10:
raise RuntimeError("counter was too big")
else:
return False
def getReward(self):
return constant.prediction(self.draft, self.radiant)
if __name__ == '__main__':
heropicks = {2: 1, 5: -1, 75: 1, 76: -1}
draft = constant.createDraft(heropicks)
radiant = []
dire = []
initialState = DraftState(draft, True)
initialState.banned.append(3)
print(initialState.banned)
mcts = mcts(timeLimit=10000)
print(initialState.draft)
# note top5 is most likely not accounted for atm
action, top5 = mcts.search(initialState)
print(action)
print("recomended hero: {0}".format(constant.getDisplayName(action)))
initialState.takeActualAction(action)
if initialState.getPossibleActions().__contains__(3):
print("ban contained in possible actions")
constant.getDisplayName(initialState.draft)
print("top5")
for hero in top5:
print(constant.getDisplayName(hero))
def playGame(self):
# sets up first two rounds
winner = None
count = 0
while (True):
count += 1
if count > 100:
break
print(count)
# goes through a round
for player in self.players:
roll = random.randint(1, 6)
roll2 = random.randint(1, 6)
sum = roll + roll2
# gives resources to players
self.distributeCards()
# Finds the current player
for player in self.players:
if (player.index == self.whoseTurn):
curPlayer = player
playerNum = player.index
# if the current player is random player
if (curPlayer.name == "Random"):
while (True):
print("Random making moves")
action = curPlayer.chooseAction(self)
if action is None:
break
else:
self.takeAction(action)
if (curPlayer.index == 1):
if (self.firstPlayerScore >= 10):
winner = curPlayer.name
break
elif (curPlayer.index == 2):
if (self.secondPlayerScore >= 10):
winner = curPlayer.name
break
elif (curPlayer.index == 3):
if (self.thirdPlayerScore >= 10):
winner = curPlayer.name
break
# if current player is Monte Carlo player
elif (curPlayer.name == "MonteCarlo"):
while (True):
print("MonteCarlo making moves")
mcts = mcts(timeLimit=1000)
action = mcts.search(self)
if action is None:
break
else:
self.takeAction(action)
if (curPlayer.index == 1):
if (self.firstPlayerScore >= 10):
winner = curPlayer.name
break
elif (curPlayer.index == 2):
if (self.secondPlayerScore >= 10):
winner = curPlayer.name
break
elif (curPlayer.index == 3):
if (self.thirdPlayerScore >= 10):
winner = curPlayer.name
break
else:
break
return winner
def mctsMove(game, depth):
initialState = ChessMCTS(game)
move = mcts.search(initialState=initialState).move
#print(move.move)
return move