def evolve_policy(self):
""" Learn a model from flightdata and evolve specialized policies. """
params = model.estimate_params(self.log.name)
noise_std = model.estimate_std(self.log.name, params)
heli = ghh.Helicopter(params, noise_std, 0.1)
genome = Genome.open(PREFIX + 'baseline.net')
self.org = functions.evolve(heli, genome, epochs=500)
def evolve_policy(self, n=1):
""" Learn a model from flightdata and evolve specialized policies. """
params = model.estimate_params(self.log.name)
noise_std = model.estimate_std(self.log.name, params)
heli = ghh.Helicopter(params, noise_std, 0.1)
genome = Genome.open(PREFIX + 'baseline.net')
for i in range(n):
champion = functions.evolve(heli, genome, epochs=500)
champion.evals = list()
self.pool.append(champion)
def evolve_policy(self, n=1):
""" Learn a model from flightdata and evolve specialized policies. """
params = model.estimate_params(self.log.name)
noise_std = model.estimate_std(self.log.name, params)
heli = ghh.Helicopter(params, noise_std, 0.1)
genome = Genome.open(PREFIX + 'baseline.net')
for i in range(n):
champion = functions.evolve(heli, genome, epochs=500)
champion.evals = list()
self.pool.append(champion)