def testScalarBounds(self):
spec = array_spec.BoundedArraySpec((), np.float, minimum=0.0, maximum=1.0)
self.assertIsInstance(spec.minimum, np.ndarray)
self.assertIsInstance(spec.maximum, np.ndarray)
# Sanity check that numpy compares correctly to a scalar for an empty shape.
self.assertEqual(0.0, spec.minimum)
self.assertEqual(1.0, spec.maximum)
# Check that the spec doesn't fail its own input validation.
_ = array_spec.BoundedArraySpec(
spec.shape, spec.dtype, spec.minimum, spec.maximum)
def _compute_action_spec(self, discrete_actions, continuous_actions):
"""Helper for `__init__`: compute our environment's action spec."""
valid_actions = []
# First discrete actions:
if discrete_actions is not None:
try:
# Get an array of upper and lower bounds for each discrete action.
min_, max_ = list(zip(*discrete_actions))
# Total number of discrete actions provided on each time step.
shape = (len(discrete_actions), )
except TypeError:
min_, max_ = discrete_actions # Enforces 2-tuple.
shape = (1, )
spec = specs.BoundedArraySpec(shape=shape,
dtype='int32',
minimum=min_,
maximum=max_,
name='discrete')
valid_actions.append(spec)
# Then continuous actions:
if continuous_actions is not None:
try:
# Get an array of upper and lower bounds for each continuous action.
min_, max_ = list(zip(*continuous_actions))
# Total number of continuous actions provided on each time step.
shape = (len(continuous_actions), )
except TypeError:
min_, max_ = continuous_actions # Enforces 2-tuple
shape = (1, )
spec = specs.BoundedArraySpec(shape=shape,
dtype='float32',
minimum=min_,
maximum=max_,
name='continuous')
valid_actions.append(spec)
# And in total we have this many actions.
action_size = sum(value.shape[0] for value in valid_actions)
if action_size <= 0:
raise ValueError(
'A pycolab Environment adapter was initialised '
'without any discrete or continuous actions specified.')
# Use arrays directly if we only have one.
if len(valid_actions) == 1:
valid_actions = valid_actions[0]
return valid_actions, action_size
def testValidateBounds(self):
spec = array_spec.BoundedArraySpec((2, 2), np.int32, minimum=5, maximum=10)
spec.validate(np.array([[5, 6], [8, 10]], dtype=np.int32))
with self.assertRaises(ValueError):
spec.validate(np.array([[5, 6], [8, 11]], dtype=np.int32))
with self.assertRaises(ValueError):
spec.validate(np.array([[4, 6], [8, 10]], dtype=np.int32))
def testNotEqualOtherClass(self):
spec_1 = array_spec.BoundedArraySpec(
(1, 2), np.int32, minimum=[0.0, -0.6], maximum=[1.0, 1.0])
spec_2 = array_spec.ArraySpec((1, 2), np.int32)
self.assertNotEqual(spec_1, spec_2)
self.assertNotEqual(spec_2, spec_1)
spec_2 = None
self.assertNotEqual(spec_1, spec_2)
self.assertNotEqual(spec_2, spec_1)
spec_2 = ()
self.assertNotEqual(spec_1, spec_2)
self.assertNotEqual(spec_2, spec_1)
def testInvalidMinimum(self):
with self.assertRaisesRegexp(ValueError, "not compatible"):
array_spec.BoundedArraySpec((3, 5), np.uint8, (0, 0, 0), (1, 1))
def testGenerateValue(self): spec = array_spec.BoundedArraySpec((2, 2), np.int32, minimum=5, maximum=10) test_value = spec.generate_value() spec.validate(test_value)
def testRepr(self):
as_string = repr(array_spec.BoundedArraySpec(
(1, 2), np.int32, minimum=101.0, maximum=73.0))
self.assertIn("101", as_string)
self.assertIn("73", as_string)
def testNotEqualDifferentMaximum(self):
spec_1 = array_spec.BoundedArraySpec(
(1, 2), np.int32, minimum=0.0, maximum=2.0)
spec_2 = array_spec.BoundedArraySpec(
(1, 2), np.int32, minimum=[0.0, 0.0], maximum=[1.0, 1.0])
self.assertNotEqual(spec_1, spec_2)
def testEqualBroadcastingBounds(self):
spec_1 = array_spec.BoundedArraySpec(
(1, 2), np.int32, minimum=0.0, maximum=1.0)
spec_2 = array_spec.BoundedArraySpec(
(1, 2), np.int32, minimum=[0.0, 0.0], maximum=[1.0, 1.0])
self.assertEqual(spec_1, spec_2)
def testNotWriteable(self):
spec = array_spec.BoundedArraySpec((1, 2, 3), np.float32, 0, (5, 5, 5))
with self.assertRaisesRegexp(ValueError, "read-only"):
spec.minimum[0] = -1
with self.assertRaisesRegexp(ValueError, "read-only"):
spec.maximum[0] = 100
def testMinMaxAttributes(self): spec = array_spec.BoundedArraySpec((1, 2, 3), np.float32, 0, (5, 5, 5)) self.assertEqual(type(spec.minimum), np.ndarray) self.assertEqual(type(spec.maximum), np.ndarray)