def evolve_policy(self): """ Evolve a specialized policy using learned model. """ pool = spawn(Genome.open(PREFIX + 'model.net'), 50) feval = functions.Evaluator(self.model) self.org = max(eonn.optimize(pool, feval.call, 2500, verbose=False)) self.org.evals = [] self.pool.append(self.org)
def evolve_policy(self):
""" Evolve a specialized policy using learned model. """
pool = spawn(Genome.open(PREFIX + "model.net"), 50)
feval = functions.Evaluator(self.model)
self.org = max(eonn.optimize(pool, feval.call, 2500, verbose=False))
self.org.evals = []
self.pool.append(self.org)
def main():
output = open("log_%s.txt" % "".join(random.sample(letters + digits, 10)), "w")
pool = organism.spawn(Genome.open(PROTOTYPE), 30)
for i in range(25):
pool, champion = optimize(pool, race_resampling, hover, 20000)
output.write("%i %.3f\n" % ((i + 1) * 20000, champion.fitness))
output.flush()
output.close()
def main():
output = open('log_%s.txt' % ''.join(random.sample(letters + digits, 10)),
'w')
pool = organism.spawn(Genome.open(PROTOTYPE), 30)
for i in range(25):
pool, champion = optimize(pool, race_resampling, hover, 20000)
output.write('%i %.3f\n' % ((i + 1) * 20000, champion.fitness))
output.flush()
output.close()
def evolve(heli, genome, popsize=50, epochs=100, keep=49, mutate_prob=.75, mutate_frac=.1, mutate_std=.8, mutate_repl=.25, verbose=False): """ Evolve a specialized policy for the given helicopter environment. """ # Set evolutionary parameters eonn.keep = keep eonn.mutate_prob = mutate_prob eonn.mutate_frac = mutate_frac eonn.mutate_std = mutate_std eonn.mutate_repl = mutate_repl # Evolve population and return champion feval = Evaluator(heli) pool = spawn(genome, popsize) return max(eonn.optimize(pool, feval.call, epochs, 1, verbose))
def evolve(heli,
genome,
popsize=50,
epochs=100,
keep=49,
mutate_prob=.75,
mutate_frac=.1,
mutate_std=.8,
mutate_repl=.25,
verbose=False):
""" Evolve a specialized policy for the given helicopter environment. """
# Set evolutionary parameters
eonn.keep = keep
eonn.mutate_prob = mutate_prob
eonn.mutate_frac = mutate_frac
eonn.mutate_std = mutate_std
eonn.mutate_repl = mutate_repl
# Evolve population and return champion
feval = Evaluator(heli)
pool = spawn(genome, popsize)
return max(eonn.optimize(pool, feval.call, epochs, 1, verbose))
