def __init__(self, config, args):
self.saver = save.Saver(config.NPOP, config.MODELDIR,
'models', 'bests', 'lawmaker', resetTol=256)
self.config, self.args = config, args
self.agentEntropies = np.repeat(self.config.ENTROPY, self.args.nRealm)
self.init()
if self.config.LOAD or self.config.BEST:
self.load(self.config.BEST)
def __init__(self, ann, config, args):
self.saver = save.Saver(config.MODELDIR,
'models',
'bests',
resetTol=256)
self.config, self.args = config, args
self.init(ann)
if self.config.LOAD or self.config.BEST:
self.load(self.config.BEST)
def __init__(self, ann, config):
self.saver = save.Saver(config.MODELDIR,
'models',
'bests',
resetTol=256)
self.config = config
print('Initializing new model...')
self.net = ann(config)
self.parameters = Parameter(
torch.Tensor(np.array(getParameters(self.net))))
def __init__(self, config, args):
self.saver = save.Saver(config.NPOP,
config.MODELDIR,
'models',
'bests',
'lawmaker',
resetTol=256)
self.config, self.args = config, args
self.nANN = config.NPOP
self.envNets = []
self.init()
if self.config.LOAD or self.config.BEST:
self.load(self.config.BEST)
def __init__(self, ann, config):
self.saver = save.Saver(config.MODELDIR,
'models',
'bests',
resetTol=256)
self.config = config
print('Initializing new model...')
self.net = ann(config)
self.parameters = Parameter(
torch.Tensor(np.array(getParameters(self.net))))
#Have been experimenting with population based
#training. Nothing stable yet -- advise avoiding
if config.POPOPT:
self.opt = PopulationOptimizer(self, config)
else:
self.opt = GradientOptimizer(self, config)
if config.LOAD or config.BEST:
self.load(self.opt, config.BEST)