def testHardOscillatorSchedule(self):
self.ScheduleTestHelper(
config_lib.Config(fn='hard_osc',
high=2,
low=0,
start_time=100,
period=10,
transition_fraction=0.5),
schedules.HardOscillatorSchedule,
[(0, 2), (1, 2), (10, 2), (100, 2), (101, 1.2), (102, 0.4),
(103, 0), (104, 0), (105, 0), (106, 0.8), (107, 1.6), (108, 2),
(109, 2), (110, 2), (111, 1.2), (112, 0.4), (115, 0), (116, 0.8),
(119, 2), (120, 2), (100001, 1.2), (100002, 0.4), (100005, 0),
(100006, 0.8), (100010, 2)])
# Test instantaneous step.
self.ScheduleTestHelper(
config_lib.Config(fn='hard_osc',
high=2,
low=0,
start_time=100,
period=10,
transition_fraction=0),
schedules.HardOscillatorSchedule, [(0, 2), (1, 2), (10, 2),
(99, 2), (100, 0), (104, 0),
(105, 2), (106, 2), (109, 2),
(110, 0)])
def make_config():
return config_lib.Config(
data=config_lib.OneOf(
[config_lib.Config(task=1, a='hello'),
config_lib.Config(task=2, a='world', b='stuff'),
config_lib.Config(task=3, c=1234)],
task=2),
model=config_lib.Config(stuff=1))
def testConfigStrictUpdateFail(self):
config = config_lib.Config(a=1, b=2, c=3, x=config_lib.Config(a=1, b=2))
with self.assertRaises(KeyError):
config.strict_update({'b': 10, 'c': 20, 'd': 50})
with self.assertRaises(KeyError):
config.strict_update(b=10, d=50)
with self.assertRaises(KeyError):
config.strict_update(x={'c': 3})
with self.assertRaises(TypeError):
config.strict_update(a='string')
with self.assertRaises(TypeError):
config.strict_update(x={'a': 'string'})
with self.assertRaises(TypeError):
config.strict_update(x=[1, 2, 3])
def testConfig(self):
config = config_lib.Config(hello='world', foo='bar', num=123, f=56.7)
self.assertEqual('world', config.hello)
self.assertEqual('bar', config['foo'])
config.hello = 'everyone'
config['bar'] = 9000
self.assertEqual('everyone', config['hello'])
self.assertEqual(9000, config.bar)
self.assertEqual(5, len(config))
def testLinearDecaySchedule(self):
self.ScheduleTestHelper(
config_lib.Config(fn='linear_decay',
initial=2,
final=0,
start_time=10,
end_time=20), schedules.LinearDecaySchedule,
[(0, 2), (1, 2), (10, 2), (11, 1.8), (15, 1), (19, 0.2), (20, 0),
(100000, 0)])
# Test step function.
self.ScheduleTestHelper(
config_lib.Config(fn='linear_decay',
initial=2,
final=0,
start_time=10,
end_time=10), schedules.LinearDecaySchedule,
[(0, 2), (1, 2), (10, 2), (11, 0), (15, 0)])
def testExponentialDecaySchedule(self):
self.ScheduleTestHelper(
config_lib.Config(fn='exp_decay',
initial=exp(-1),
final=exp(-6),
start_time=10,
end_time=20), schedules.ExponentialDecaySchedule,
[(0, exp(-1)), (1, exp(-1)), (10, exp(-1)), (11, exp(-1 / 2. - 1)),
(15, exp(-5 / 2. - 1)), (19, exp(-9 / 2. - 1)), (20, exp(-6)),
(100000, exp(-6))])
# Test step function.
self.ScheduleTestHelper(
config_lib.Config(fn='exp_decay',
initial=exp(-1),
final=exp(-6),
start_time=10,
end_time=10), schedules.ExponentialDecaySchedule,
[(0, exp(-1)), (1, exp(-1)), (10, exp(-1)), (11, exp(-6)),
(15, exp(-6))])
def testSmootherstepDecaySchedule(self):
self.ScheduleTestHelper(
config_lib.Config(fn='smooth_decay',
initial=2,
final=0,
start_time=10,
end_time=20),
schedules.SmootherstepDecaySchedule, [(0, 2), (1, 2), (10, 2),
(11, 1.98288), (15, 1),
(19, 0.01712), (20, 0),
(100000, 0)])
# Test step function.
self.ScheduleTestHelper(
config_lib.Config(fn='smooth_decay',
initial=2,
final=0,
start_time=10,
end_time=10),
schedules.SmootherstepDecaySchedule, [(0, 2), (1, 2), (10, 2),
(11, 0), (15, 0)])
def default_config():
return config_lib.Config(
agent=config_lib.OneOf(
[
config_lib.Config(
algorithm='pg',
policy_lstm_sizes=[35, 35],
# Set value_lstm_sizes to None to share weights with policy.
value_lstm_sizes=[35, 35],
obs_embedding_size=10,
grad_clip_threshold=10.0,
param_init_factor=1.0,
lr=5e-5,
pi_loss_hparam=1.0,
vf_loss_hparam=0.5,
entropy_beta=1e-2,
regularizer=0.0,
softmax_tr=1.0, # Reciprocal temperature.
optimizer='rmsprop', # 'adam', 'sgd', 'rmsprop'
topk=0, # Top-k unique codes will be stored.
topk_loss_hparam=0.0, # off policy loss multiplier.
# Uniformly sample this many episodes from topk buffer per batch.
# If topk is 0, this has no effect.
topk_batch_size=1,
# Exponential moving average baseline for REINFORCE.
# If zero, A2C is used.
# If non-zero, should be close to 1, like .99, .999, etc.
ema_baseline_decay=0.99,
# Whether agent can emit EOS token. If true, agent can emit EOS
# token which ends the episode early (ends the sequence).
# If false, agent must emit tokens until the timestep limit is
# reached. e.g. True means variable length code, False means fixed
# length code.
# WARNING: Making this false slows things down.
eos_token=False,
replay_temperature=1.0,
# Replay probability. 1 = always replay, 0 = always on policy.
alpha=0.0,
# Whether to normalize importance weights in each minibatch.
iw_normalize=True),
config_lib.Config(
algorithm='ga', crossover_rate=0.99, mutation_rate=0.086),
config_lib.Config(algorithm='rand')
],
algorithm='pg',
),
env=config_lib.Config(
# If True, task-specific settings are not needed.
task='', # 'print', 'echo', 'reverse', 'remove', ...
task_cycle=[], # If non-empty, reptitions will cycle through tasks.
task_kwargs='{}', # Python dict literal.
task_manager_config=config_lib.Config(
# Reward recieved per test case. These bonuses will be scaled
# based on how many test cases there are.
correct_bonus=2.0, # Bonus for code getting correct answer.
code_length_bonus=1.0), # Maximum bonus for short code.
correct_syntax=False,
),
batch_size=64,
timestep_limit=32)
def make_config():
return config_lib.Config(
data=config_lib.OneOf(
[config_lib.Config(task=1, a='hello'),
config_lib.Config(
task=2,
a=config_lib.OneOf(
[config_lib.Config(x=1, y=2),
config_lib.Config(x=-1, y=1000, z=4)],
x=1)),
config_lib.Config(task=3, c=1234)],
task=2),
model=config_lib.Config(stuff=1))
def __init__(self, config):
super(ExponentialDecaySchedule, self).__init__(config)
self.initial = config.initial
self.final = config.final
self.start_time = config.start_time
self.end_time = config.end_time
if self.initial <= 0 or self.final <= 0:
raise ValueError('initial and final must be positive numbers.')
# Linear interpolation in log space.
self._linear_fn = LinearDecaySchedule(
config_lib.Config(initial=math.log(self.initial),
final=math.log(self.final),
start_time=self.start_time,
end_time=self.end_time))
def testNestedOneOf(self):
def make_config():
return config_lib.Config(
data=config_lib.OneOf(
[config_lib.Config(task=1, a='hello'),
config_lib.Config(
task=2,
a=config_lib.OneOf(
[config_lib.Config(x=1, y=2),
config_lib.Config(x=-1, y=1000, z=4)],
x=1)),
config_lib.Config(task=3, c=1234)],
task=2),
model=config_lib.Config(stuff=1))
config = make_config()
config.update(config_lib.Config.parse(
'model=c(stuff=2),data=c(task=2,a=c(x=-1,z=8))'))
self.assertEqual(
config_lib.Config(
data=config_lib.Config(
task=2,
a=config_lib.Config(x=-1, y=1000, z=8)),
model=config_lib.Config(stuff=2)),
config)
config = make_config()
config.strict_update(config_lib.Config.parse(
'model=c(stuff=2),data=c(task=2,a=c(x=-1,z=8))'))
self.assertEqual(
config_lib.Config(
data=config_lib.Config(
task=2,
a=config_lib.Config(x=-1, y=1000, z=8)),
model=config_lib.Config(stuff=2)),
config)
config = make_config()
config.update(config_lib.Config.parse('model=c(stuff=2)'))
self.assertEqual(
config_lib.Config(
data=config_lib.Config(
task=2,
a=config_lib.Config(x=1, y=2)),
model=config_lib.Config(stuff=2)),
config)
config = make_config()
config.strict_update(config_lib.Config.parse('model=c(stuff=2)'))
self.assertEqual(
config_lib.Config(
data=config_lib.Config(
task=2,
a=config_lib.Config(x=1, y=2)),
model=config_lib.Config(stuff=2)),
config)
def testOneOfStrict(self):
def make_config():
return config_lib.Config(
data=config_lib.OneOf(
[config_lib.Config(task=1, a='hello'),
config_lib.Config(task=2, a='world', b='stuff'),
config_lib.Config(task=3, c=1234)],
task=2),
model=config_lib.Config(stuff=1))
config = make_config()
config.strict_update(config_lib.Config.parse(
'model=c(stuff=2),data=c(task=1,a="hi")'))
self.assertEqual(
config_lib.Config(
data=config_lib.Config(task=1, a='hi'),
model=config_lib.Config(stuff=2)),
config)
config = make_config()
config.strict_update(config_lib.Config.parse(
'model=c(stuff=2),data=c(task=2,a="hi")'))
self.assertEqual(
config_lib.Config(
data=config_lib.Config(task=2, a='hi', b='stuff'),
model=config_lib.Config(stuff=2)),
config)
config = make_config()
config.strict_update(config_lib.Config.parse(
'model=c(stuff=2),data=c(task=3)'))
self.assertEqual(
config_lib.Config(
data=config_lib.Config(task=3, c=1234),
model=config_lib.Config(stuff=2)),
config)
config = make_config()
config.strict_update(config_lib.Config.parse(
'model=c(stuff=2)'))
self.assertEqual(
config_lib.Config(
data=config_lib.Config(task=2, a='world', b='stuff'),
model=config_lib.Config(stuff=2)),
config)
def testConfigUpdate(self):
config = config_lib.Config(a=1, b=2, c=3)
config.update({'b': 10, 'd': 4})
self.assertEqual({'a': 1, 'b': 10, 'c': 3, 'd': 4}, config)
config = config_lib.Config(a=1, b=2, c=3)
config.update(b=10, d=4)
self.assertEqual({'a': 1, 'b': 10, 'c': 3, 'd': 4}, config)
config = config_lib.Config(a=1, b=2, c=3)
config.update({'e': 5}, b=10, d=4)
self.assertEqual({'a': 1, 'b': 10, 'c': 3, 'd': 4, 'e': 5}, config)
config = config_lib.Config(
a=1,
b=2,
x=config_lib.Config(
l='a',
y=config_lib.Config(m=1, n=2),
z=config_lib.Config(
q=config_lib.Config(a=10, b=20),
r=config_lib.Config(s=1, t=2))))
config.update(x={'y': {'m': 10}, 'z': {'r': {'s': 5}}})
self.assertEqual(
config_lib.Config(
a=1, b=2,
x=config_lib.Config(
l='a',
y=config_lib.Config(m=10, n=2),
z=config_lib.Config(
q=config_lib.Config(a=10, b=20),
r=config_lib.Config(s=5, t=2)))),
config)
config = config_lib.Config(
foo='bar',
num=100,
x=config_lib.Config(a=1, b=2, c=config_lib.Config(h=10, i=20, j=30)),
y=config_lib.Config(qrs=5, tuv=10),
d={'a': 1, 'b': 2},
l=[1, 2, 3])
config.update(
config_lib.Config(
foo='hat',
num=50.5,
x={'a': 5, 'z': -10},
y=config_lib.Config(wxyz=-1)),
d={'a': 10, 'c': 20},
l=[3, 4, 5, 6])
self.assertEqual(
config_lib.Config(
foo='hat',
num=50.5,
x=config_lib.Config(a=5, b=2, z=-10,
c=config_lib.Config(h=10, i=20, j=30)),
y=config_lib.Config(qrs=5, tuv=10, wxyz=-1),
d={'a': 10, 'c': 20},
l=[3, 4, 5, 6]),
config)
self.assertTrue(isinstance(config.x, config_lib.Config))
self.assertTrue(isinstance(config.x.c, config_lib.Config))
self.assertTrue(isinstance(config.y, config_lib.Config))
config = config_lib.Config(
foo='bar',
num=100,
x=config_lib.Config(a=1, b=2, c=config_lib.Config(h=10, i=20, j=30)),
y=config_lib.Config(qrs=5, tuv=10),
d={'a': 1, 'b': 2},
l=[1, 2, 3])
config.update(
config_lib.Config(
foo=1234,
num='hello',
x={'a': 5, 'z': -10, 'c': {'h': -5, 'k': 40}},
y=[1, 2, 3, 4],
d='stuff',
l={'a': 1, 'b': 2}))
self.assertEqual(
config_lib.Config(
foo=1234,
num='hello',
x=config_lib.Config(a=5, b=2, z=-10,
c=config_lib.Config(h=-5, i=20, j=30, k=40)),
y=[1, 2, 3, 4],
d='stuff',
l={'a': 1, 'b': 2}),
config)
self.assertTrue(isinstance(config.x, config_lib.Config))
self.assertTrue(isinstance(config.x.c, config_lib.Config))
self.assertTrue(isinstance(config.y, list))
def testConfigStrictUpdate(self):
config = config_lib.Config(a=1, b=2, c=3)
config.strict_update({'b': 10, 'c': 20})
self.assertEqual({'a': 1, 'b': 10, 'c': 20}, config)
config = config_lib.Config(a=1, b=2, c=3)
config.strict_update(b=10, c=20)
self.assertEqual({'a': 1, 'b': 10, 'c': 20}, config)
config = config_lib.Config(a=1, b=2, c=3, d=4)
config.strict_update({'d': 100}, b=10, a=20)
self.assertEqual({'a': 20, 'b': 10, 'c': 3, 'd': 100}, config)
config = config_lib.Config(
a=1,
b=2,
x=config_lib.Config(
l='a',
y=config_lib.Config(m=1, n=2),
z=config_lib.Config(
q=config_lib.Config(a=10, b=20),
r=config_lib.Config(s=1, t=2))))
config.strict_update(x={'y': {'m': 10}, 'z': {'r': {'s': 5}}})
self.assertEqual(
config_lib.Config(
a=1, b=2,
x=config_lib.Config(
l='a',
y=config_lib.Config(m=10, n=2),
z=config_lib.Config(
q=config_lib.Config(a=10, b=20),
r=config_lib.Config(s=5, t=2)))),
config)
config = config_lib.Config(
foo='bar',
num=100,
x=config_lib.Config(a=1, b=2, c=config_lib.Config(h=10, i=20, j=30)),
y=config_lib.Config(qrs=5, tuv=10),
d={'a': 1, 'b': 2},
l=[1, 2, 3])
config.strict_update(
config_lib.Config(
foo='hat',
num=50,
x={'a': 5, 'c': {'h': 100}},
y=config_lib.Config(tuv=-1)),
d={'a': 10, 'c': 20},
l=[3, 4, 5, 6])
self.assertEqual(
config_lib.Config(
foo='hat',
num=50,
x=config_lib.Config(a=5, b=2,
c=config_lib.Config(h=100, i=20, j=30)),
y=config_lib.Config(qrs=5, tuv=-1),
d={'a': 10, 'c': 20},
l=[3, 4, 5, 6]),
config)
def testConstSchedule(self):
self.ScheduleTestHelper(config_lib.Config(fn='const', const=5),
schedules.ConstSchedule, [(0, 5), (1, 5),
(10, 5), (20, 5),
(100, 5),
(1000000, 5)])
