def reset(self):
EpisodicTask.reset(self)
# sleep(3)
# EpisodicTask.reset(self)
# sleep(3)
self.finished = False
self.reward = 0
self.status = 0
def __init__(self, size, opponent=None, **args):
EpisodicTask.__init__(self, PenteGame((size, size)))
self.setArgs(**args)
if opponent == None:
opponent = RandomGomokuPlayer(self.env)
elif isclass(opponent):
# assume the agent can be initialized without arguments then.
opponent = opponent(self.env)
if not self.opponentStart:
opponent.color = PenteGame.WHITE
self.opponent = opponent
self.minmoves = 9
self.maxmoves = self.env.size[0] * self.env.size[1]
self.reset()
def getObservation(self):
obs = EpisodicTask.getObservation(self)
if len(obs) == MarioEnvironment.numberOfFitnessValues:
self.reward = obs[1]
self.status = obs[0]
self.finished = True
return obs
def __init__(self, size, opponent = None, **args):
EpisodicTask.__init__(self, PenteGame((size, size)))
self.setArgs(**args)
if opponent == None:
opponent = RandomGomokuPlayer(self.env)
elif isclass(opponent):
# assume the agent can be initialized without arguments then.
opponent = opponent(self.env)
if not self.opponentStart:
opponent.color = PenteGame.WHITE
self.opponent = opponent
self.minmoves = 9
self.maxmoves = self.env.size[0] * self.env.size[1]
self.reset()
def performAction(self, action):
if not self.isFinished():
EpisodicTask.performAction(self, action)
def __init__(self, *args, **kwargs):
EpisodicTask.__init__(self, MarioEnvironment(*args, **kwargs))
