def run_tests(cfg):
start_time = time.time()
try:
cfg.update(defaultcfg, overwrite = False)
data = datafile.load_data(cfg.hardware.datafile)
date, expcfg, history = data['date'], data['cfg'], data['history']
cfg.test.setdefault('max', value = len(history))
cfg.test.max = min(len(history), cfg.test.max)
cfg.update(expcfg, overwrite = False)
bot = robots.build_robot(cfg.sim)
assert cfg.goals.effect.s_feats == bot.s_feats
assert cfg.goals.effect.s_bounds == bot.s_bounds
assert cfg.goals.motor.m_feats == bot.m_feats
assert cfg.goals.motor.m_bounds == bot.m_bounds
learner = Learner(bot.m_feats, bot.s_feats, bot.m_bounds, fwd = 'ES-LWLR', inv = 'L-BFGS-B')
if cfg.test.testsetfile is None:
testset = exp.create_tests(bot.s_bounds, cfg.test.res, fixed_dim = cfg.test.fixed_dim)
else:
testset = datafile.load_testset(cfg.test.testsetfile)
print(cfg.makeReport())
ticks, results, averages, stds = [], [], [], []
for counter, (order, goal, _, effect) in enumerate(history):
if (counter) % cfg.test.freq == 0:
ticks.append(counter)
result = exp.test_results(bot, learner, testset)
avg, std = exp.test_perfs(testset, result)
results.append(result)
averages.append(avg)
stds.append(std)
print("{}{:5d}: {}{:6.4f}{}".format(gfx.purple, counter, gfx.cyan, avg, gfx.end))
if counter > cfg.test.max:
break
learner.add_xy(order, effect)
exp.save_test(cfg, date, tuple(testset), tuple(ticks), tuple(results),
tuple(averages), tuple(stds))
except Exception:
import traceback
traceback.print_exc()
finally:
bot.close()
print("ran for {}s.".format(int(time.time() - start_time)))
def _main():
# Set random seed for training
seed = 8964
tf.set_random_seed(seed)
np.random.seed(seed)
random.seed(seed)
pp = pprint.PrettyPrinter()
print_flags_dict = {}
for key in FLAGS.__flags.keys():
print_flags_dict[key] = getattr(FLAGS, key)
pp.pprint(print_flags_dict)
if not os.path.exists(FLAGS.checkpoint_dir):
os.makedirs(FLAGS.checkpoint_dir)
assert os.path.exists(FLAGS.train_dir), "Specify training data directory!"
assert os.path.exists(FLAGS.val_dir), "Specify validation data directory!"
trl = Learner()
trl.train(FLAGS)
def run_second(cfg):
start_time = time.time()
try:
if cfg.hardware.seed.get('secondary', None) is None:
seed = random.randint(0, sys.maxint)
cfg.hardware.seed.secondary = seed
random.seed(cfg.hardware.seed.secondary)
data = datafile.load_data(cfg.hardware.primaryfile)
date, primcfg, history = data['date'], data['cfg'], data['history']
cfg.update(primcfg, overwrite=False)
primcfg = primcfg.copy(deep=True)
bot = robots.build_robot(cfg.sim)
# this should not change
assert cfg.goals.motor.m_feats == bot.m_feats, 'expected {}, got {}'.format(
cfg.goals.motor.m_feats, bot.m_feats)
assert cfg.goals.motor.m_bounds == bot.m_bounds
# sensors might change
cfg.goals.effect.s_feats = bot.s_feats
cfg.goals.effect.s_bounds = bot.s_bounds
## Configuration ##
cfg.goals.effect.res = 10
## Bias loading ##
grid_expl = GridExplorer(primcfg.goals.effect.s_feats,
cfg=primcfg.goals)
dataset = []
for order, goal, _, effect in history:
effect = [
min(b_max, max(b_min, e_i))
for e_i, (b_min,
b_max) in zip(effect, primcfg.goals.effect.s_bounds)
]
grid_expl.add_effect(effect, goal=goal)
dataset.append((order, effect))
bs = bias.IMBias(dataset, effectexplorer=grid_expl)
## Instanciation of core modules ##
#s_explorer = bias.GridBias(bs, cfg.effect.cfg.interest.max_deriv, s_feats, cfg = cfg.effect.cfg)
s_explorer = BoundedRandomExplorer(bot.s_feats, cfg.goals)
m_babble = MotorBabble(bot.m_feats, bot.m_bounds, cfg.goals)
m_explorer = bias.MotorBias(bs, m_babble, cfg.goals)
guide = Guide(bot.m_feats,
bot.s_feats,
bot.m_bounds,
cfg.goals,
goalexplorer=s_explorer,
motorbabble=m_explorer)
learner = Learner(bot.m_feats,
bot.s_feats,
bot.m_bounds,
fwd='ES-LWLR',
inv='L-BFGS-B')
cfg.freeze()
print(cfg.makeReport())
## Running learning ##
for i in xrange(cfg.exp.trials):
exp.learn(bot, guide, learner)
gfx.print_progress(i + 1,
cfg.exp.trials,
prefix='{}learning... {}'.format(
gfx.purple, gfx.cyan),
suffix=gfx.end,
eta=1)
print ''
exp.save_data(cfg, guide)
except Exception:
import traceback
traceback.print_exc()
finally:
bot.close()
print("ran for {}s.".format(int(time.time() - start_time)))
""" Inverse model benchmark. Inverse model performance is tested in function of the forward model, the inverse and forward parameters and the robots. """ import random random.seed(30) import robots from models.learner import Learner from models.plots.ercurve import ErrorCurve # Robot arm = robots.KinematicArm2D(dimM = 6) # Learner invlearners = ([Learner.from_robot(arm, fwd = 'WNN', inv = 'BFGS', k = 5, sigma = 5.0)] #+ [Learner.from_robot(arm, fwd = 'WNN', inv = 'BFGS', sigma = 10.0)] #+ [Learner.from_robot(arm, fwd = 'AvgNN', inv = 'BFGS', k = 5, sigma = 5.0)] + [Learner.from_robot(arm, fwd = 'ES-LWLR', inv = 'BFGS', k = 20)] ) # Plot curve = ErrorCurve(arm, invlearners, side = 'inverse', trials = 1000, tests = 250, uniformity = 'sensor') curve.plot() curve.show()
Inverse model performance is tested in function of the forward model,
the inverse and forward parameters and the robots.
"""
import random
random.seed(30)
import robots
from models.learner import Learner
from models.plots.ercurve import ErrorCurve
# Robot
arm = robots.KinematicArm2D(dimM=6)
# Learner
invlearners = (
[Learner.from_robot(arm, fwd='WNN', inv='BFGS', k=5, sigma=5.0)]
#+ [Learner.from_robot(arm, fwd = 'WNN', inv = 'BFGS', sigma = 10.0)]
#+ [Learner.from_robot(arm, fwd = 'AvgNN', inv = 'BFGS', k = 5, sigma = 5.0)]
+ [Learner.from_robot(arm, fwd='ES-LWLR', inv='BFGS', k=20)])
# Plot
curve = ErrorCurve(arm,
invlearners,
side='inverse',
trials=1000,
tests=250,
uniformity='sensor')
curve.plot()
curve.show()
def run_first(cfg):
start_time = time.time()
try:
if cfg.hardware.seed.get('primary', None) is None:
seed = random.randint(0, sys.maxint)
cfg.hardware.seed.primary = seed
random.seed(cfg.hardware.seed.primary)
bot = robots.build_robot(cfg.sim)
## Configuration ##
goalscfg = treedict.TreeDict()
goalscfg.effect.explorer = BoundedRandomExplorer
cfg.goals.update(goalscfg, overwrite = False)
cfg.goals.effect.s_feats = s_feats = bot.s_feats
cfg.goals.effect.s_bounds = s_bounds = bot.s_bounds
cfg.goals.motor.m_feats = m_feats = bot.m_feats
cfg.goals.motor.m_bounds = m_bounds = bot.m_bounds
## Instanciation of core modules ##
s_explorer = BoundedRandomExplorer(s_feats, cfg.goals)
if 'explorename' in cfg.goals.effect:
if cfg.goals.effect.explorername == 'cluster':
s_explorer = cluster.ClusterExplorer(s_feats, cfg.goals)
elif cfg.goals.effect.explorername == 'cluster_im':
s_explorer = cluster.ClusterIMExplorer(s_feats, cfg.goals)
m_explorer = MotorBabble(bot.m_feats, bot.m_bounds, cfg.goals)
guide = Guide(m_feats, s_feats, m_bounds, cfg.goals,
goalexplorer = s_explorer, motorbabble = m_explorer)
learner = Learner(m_feats, s_feats, m_bounds, fwd = 'ES-LWLR', inv = 'L-BFGS-B')
cfg.freeze()
print(cfg.makeReport())
## Runing learning ##
for i in xrange(cfg.exp.trials):
exp.learn(bot, guide, learner)
if not cfg.sim.verbose:
gfx.print_progress(i+1, cfg.exp.trials,
prefix = '{}learning... {}'.format(gfx.purple, gfx.cyan),
suffix = gfx.end, eta = 1)
print ''
exp.save_data(cfg, guide)
except Exception:
import traceback
traceback.print_exc()
finally:
bot.close()
print("ran for {}s.".format(int(time.time() - start_time)))
"""Benchmarking a simple 3 DOF, 2D Kinematic Arm in function of timesteps.""" import random random.seed(0) import robots import models from models.learner import Learner, fwdclass from models.plots.ercurve import ErrorCurve #models.disable_cmodels() # Robot arm = robots.KinematicArm2D(dim = 3) #fwdlearners = [Learner.from_robot(arm, fwd = fwd, inv = 'NN', k = 5, sigma = 5.0) for fwd in fwdclass] fwdlearners = [] #fwdlearners.append(Learner.from_robot(arm, fwd = 'ES-LWLR', inv = 'NN', k = 4)) #fwdlearners.append(Learner.from_robot(arm, fwd = 'ES-LWLR', inv = 'NN', k = 7)) fwdlearners.append(Learner.from_robot(arm, fwd = 'ES-LWLR', inv = 'NN', k = 3)) fwdlearners.append(Learner.from_robot(arm, fwd = 'ES-LWLR', inv = 'NN', k = 7)) fwdlearners.append(Learner.from_robot(arm, fwd = 'ES-LWLR', inv = 'NN', k = 10)) fwdlearners.append(Learner.from_robot(arm, fwd = 'ES-LWLR', inv = 'NN', k = 20)) curve = ErrorCurve(arm, fwdlearners, side = 'forward', trials = 2000, tests = 200, uniformity = 'motor', test_period = 5, test_pace = 1.5) curve.plot() curve.show()
"""Benchmarking a simple 3 DOF, 2D Kinematic Arm in function of timesteps.""" import random random.seed(0) import forest import robots from models.learner import Learner, fwdclass from models.plots.ercurve import ErrorCurve # Robot cfg = forest.Tree() cfg.dim = 6 cfg.lengths = 1.0 cfg.limits = (-360.0, 360.0) arm = robots.KinematicArm2D(cfg) fwdlearners = [] fwdlearners.append(Learner.from_robot(arm, fwd = 'ES-LWLR', inv = 'L-BFGS-B', k = 2*cfg.dim+1)) curve = ErrorCurve(arm, fwdlearners, side = 'forward', trials = 50000, tests = 200, uniformity = 'motor') curve.plot() curve.show()
def run_tests(cfg):
start_time = time.time()
try:
cfg.update(defaultcfg, overwrite=False)
data = datafile.load_data(cfg.hardware.datafile)
date, expcfg, history = data['date'], data['cfg'], data['history']
cfg.test.setdefault('max', value=len(history))
cfg.test.max = min(len(history), cfg.test.max)
cfg.update(expcfg, overwrite=False)
bot = robots.build_robot(cfg.sim)
assert cfg.goals.effect.s_feats == bot.s_feats
assert cfg.goals.effect.s_bounds == bot.s_bounds
assert cfg.goals.motor.m_feats == bot.m_feats
assert cfg.goals.motor.m_bounds == bot.m_bounds
learner = Learner(bot.m_feats,
bot.s_feats,
bot.m_bounds,
fwd='ES-LWLR',
inv='L-BFGS-B')
if cfg.test.testsetfile is None:
testset = exp.create_tests(bot.s_bounds,
cfg.test.res,
fixed_dim=cfg.test.fixed_dim)
else:
testset = datafile.load_testset(cfg.test.testsetfile)
print(cfg.makeReport())
ticks, results, averages, stds = [], [], [], []
for counter, (order, goal, _, effect) in enumerate(history):
if (counter) % cfg.test.freq == 0:
ticks.append(counter)
result = exp.test_results(bot, learner, testset)
avg, std = exp.test_perfs(testset, result)
results.append(result)
averages.append(avg)
stds.append(std)
print("{}{:5d}: {}{:6.4f}{}".format(gfx.purple, counter,
gfx.cyan, avg, gfx.end))
if counter > cfg.test.max:
break
learner.add_xy(order, effect)
exp.save_test(cfg, date, tuple(testset), tuple(ticks), tuple(results),
tuple(averages), tuple(stds))
except Exception:
import traceback
traceback.print_exc()
finally:
bot.close()
print("ran for {}s.".format(int(time.time() - start_time)))
import robots
import models
from models.learner import Learner, fwdclass
from models.plots.ercurve import ErrorCurve
#models.disable_cmodels()
# Robot
arm = robots.KinematicArm2D(dim=3)
#fwdlearners = [Learner.from_robot(arm, fwd = fwd, inv = 'NN', k = 5, sigma = 5.0) for fwd in fwdclass]
fwdlearners = []
#fwdlearners.append(Learner.from_robot(arm, fwd = 'ES-LWLR', inv = 'NN', k = 4))
#fwdlearners.append(Learner.from_robot(arm, fwd = 'ES-LWLR', inv = 'NN', k = 7))
fwdlearners.append(Learner.from_robot(arm, fwd='ES-LWLR', inv='NN', k=3))
fwdlearners.append(Learner.from_robot(arm, fwd='ES-LWLR', inv='NN', k=7))
fwdlearners.append(Learner.from_robot(arm, fwd='ES-LWLR', inv='NN', k=10))
fwdlearners.append(Learner.from_robot(arm, fwd='ES-LWLR', inv='NN', k=20))
curve = ErrorCurve(arm,
fwdlearners,
side='forward',
trials=2000,
tests=200,
uniformity='motor',
test_period=5,
test_pace=1.5)
curve.plot()
curve.show()
def create_learner(self):
learner = Learner(self, self.generate_id())
self.learners.append(learner)