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))