def test_unflatten_n_tuple(self):
box = Box(0.0, 1.0, (3, 4))
obs = ((([1, 2, 3], [3, 4, 5], [5, 6, 7],
[7, 8, 9]), ([1, 2, 3], [3, 4, 5], [5, 6, 7], [7, 8, 9]),
([1, 2, 3], [3, 4, 5], [5, 6, 7], [7, 8, 9])))
arr = box.unflatten_n(obs)
assert arr.shape == (3, 3, 4)
def test_same_dtype(self):
type1 = np.float32
box = Box(0, 255, (3, 4), type1)
assert box.dtype == type1
type2 = np.uint8
box = Box(0, 255, (3, 4), type2)
assert box.dtype == type2
def test_unflatten_n(self):
box = Box(0.0, 1.0, (3, 4))
obs = np.asarray((([1, 2, 3], [3, 4, 5], [5, 6, 7], [7, 8, 9]),
([1, 2, 3], [3, 4, 5], [5, 6, 7], [7, 8, 9]),
([1, 2, 3], [3, 4, 5], [5, 6, 7], [7, 8, 9])))
arr = box.unflatten_n(obs)
assert obs.shape == (3, 4, 3)
assert arr.shape == (3, 3, 4)
def test_flatten(self):
d = Dict(
collections.OrderedDict([('position', Box(0, 10, (2, ))),
('velocity', Box(0, 10, (3, )))]))
f = np.array([1., 2., 3., 4., 5.])
# Keys are intentionally in the "wrong" order.
s = collections.OrderedDict([('velocity', np.array([3., 4., 5.])),
('position', np.array([1., 2.]))])
assert (d.flatten(s) == f).all()
def test_unflatten(self):
d = Dict(
collections.OrderedDict([('position', Box(0, 10, (2, ))),
('velocity', Box(0, 10, (3, )))]))
f = np.array([1., 2., 3., 4., 5.])
# Keys are intentionally in the "wrong" order.
s = collections.OrderedDict([('velocity', np.array([3., 4., 5.])),
('position', np.array([1., 2.]))])
assert all((s[k] == v).all() for k, v in d.unflatten(f).items())
def test_concat(self):
d1 = Dict(
collections.OrderedDict([('position', Box(0, 10, (2, ))),
('velocity', Box(0, 10, (3, )))]))
d2 = Dict(
collections.OrderedDict([('position', Box(0, 10, (2, ))),
('gravity', Box(0, 10, (3, )))]))
concat_d = d1.concat(d2)
assert (sorted(concat_d.spaces.keys()) == sorted(
['position', 'velocity', 'gravity']))
def test_unflatten_n(self):
d = Dict(
collections.OrderedDict([('position', Box(0, 10, (2, ))),
('velocity', Box(0, 10, (3, )))]))
f = np.array([[1., 2., 3., 4., 5.], [6., 7., 8., 9., 0.]])
# Keys are intentionally in the "wrong" order.
s = [
collections.OrderedDict([('velocity', np.array([3., 4., 5.])),
('position', np.array([1., 2.]))]),
collections.OrderedDict([('velocity', np.array([8., 9., 0.])),
('position', np.array([6., 7.]))])
]
for i, fi in enumerate(d.unflatten_n(f)):
assert all((s[i][k] == v).all() for k, v in fi.items())
def test_concat(self):
box1 = Box(0.0, 1.0, (3, 4))
box2 = Box(1.0, 2.0, (2, 3))
concat_box = box1.concat(box2)
expected_shape = box1.flat_dim + box2.flat_dim
assert concat_box.shape == expected_shape
assert np.array_equal(
concat_box.low,
np.concatenate([np.zeros(box1.flat_dim),
np.ones(box2.flat_dim)]))
assert np.array_equal(
concat_box.high,
np.concatenate([np.ones(box1.flat_dim),
np.full(box2.flat_dim, 2)]))
def test_pickleable(self):
obj = Box(-1.0, 1.0, (3, 4))
round_trip = pickle.loads(pickle.dumps(obj))
assert round_trip
assert round_trip.shape == obj.shape
assert np.array_equal(round_trip.bounds[0], obj.bounds[0])
assert np.array_equal(round_trip.bounds[1], obj.bounds[1])
def test_convert_theano(self):
tup = Tuple((Box(0.0, 1.0, (3, 4)), Discrete(2)))
tensor_tup = tup.to_theano_tensor('test', 1)
assert isinstance(tensor_tup, tuple)
assert all(
[isinstance(c, theano.tensor.TensorVariable) for c in tensor_tup])
assert [c.dtype for c in tensor_tup] == ['float32', 'int64']
def test_bounds(self):
box = Box(0.0, 1.0, (3, 4))
low, high = box.bounds
assert low.shape == (3, 4)
assert high.shape == (3, 4)
assert low.dtype == np.float32
assert high.dtype == np.float32
def test_convert_tf(self):
tup = Tuple((Box(0.0, 1.0, (3, 4)), Discrete(2)))
tensor_tup = tup.to_tf_placeholder('test', 1)
assert isinstance(tensor_tup, tuple)
assert all([isinstance(c, tf.Tensor) for c in tensor_tup])
assert [c.dtype for c in tensor_tup] == [tf.float32, tf.int64]
assert [c.get_shape().as_list() for c in tensor_tup] == [[None, 3, 4],
[None, 2]]
def _to_akro_space(self, space):
"""
Converts a gym.space into an akro.space.
Args:
space (gym.spaces)
Returns:
space (akro.spaces)
"""
if isinstance(space, GymBox):
return Box(low=space.low, high=space.high, dtype=space.dtype)
elif isinstance(space, GymDict):
return Dict(space.spaces)
elif isinstance(space, GymDiscrete):
return Discrete(space.n)
elif isinstance(space, GymTuple):
return Tuple(list(map(self._to_akro_space, space.spaces)))
else:
raise NotImplementedError
def test_invalid_env(self):
with self.assertRaises(ValueError):
Box(0.0, 1.0)
with self.assertRaises(Exception):
Box(np.array([-1.0, -2.0]), np.array([1.0, 2.0]), (2, 2))
def test_hash(self):
box1 = Box(0.0, 1.0, (3, 4))
box2 = Box(0.0, 1.0, (3, 4))
assert box1.__hash__() == box2.__hash__()
def test_concat(self):
tup1 = Tuple((Box(0, 1, (5, )), Box(0, 1, (10, ))))
tup2 = Tuple((Box(0, 1, (5, )), Box(0, 1, (10, ))))
concat_tup = tup1.concat(tup2)
assert concat_tup.flat_dim == 30
def test_flat_dim(self):
d = Dict(
collections.OrderedDict(position=Box(0, 10, (2, )),
velocity=Box(0, 10, (3, ))))
assert d.flat_dim == 5
def test_flat_dim_with_keys(self):
d = Dict(
collections.OrderedDict([('position', Box(0, 10, (2, ))),
('velocity', Box(0, 10, (3, )))]))
assert d.flat_dim_with_keys(['position']) == 2
def test_convert_theano(self):
box = Box(0.0, 1.0, (3, 4))
tensor = box.to_theano_tensor('test', 1)
assert isinstance(tensor, theano.tensor.TensorVariable)
assert box.dtype == np.float32
assert tensor.dtype == 'float32'
def test_convert_tf(self):
box = Box(0.0, 1.0, (3, 4))
tensor = box.to_tf_placeholder('test', 1)
assert isinstance(tensor, tf.Tensor)
assert tensor.dtype == tf.float32
assert tensor.get_shape().as_list() == [None, 3, 4]
def test_unflatten(self):
box = Box(0.0, 1.0, (3, 4))
arr = box.unflatten([(1, 2, 3), (4, 5, 6), (7, 8, 9), (10, 11, 12)])
assert arr.shape == (3, 4)
assert arr.dtype == np.int64
def test_flatten(self):
box = Box(0.0, 1.0, (3, 4))
arr = [[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]]
flat_arr = box.flatten(arr)
assert flat_arr.shape == (12, )
assert flat_arr.dtype == np.int64
def test_pickleable(self):
env_spec = EnvSpec(Box(-1, 1, (1)), Box(-2, 2, (2)))
round_trip = pickle.loads(pickle.dumps(env_spec))
assert round_trip
assert round_trip.action_space == env_spec.action_space
assert round_trip.observation_space == env_spec.observation_space
def test_flat_dim(self):
box = Box(0.0, 1.0, (3, 4))
assert box.flat_dim == 12
def test_invalid_env(self):
with self.assertRaises(AttributeError):
Box(0.0, 1.0)
with self.assertRaises(AssertionError):
Box(np.array([-1.0, -2.0]), np.array([1.0, 2.0]), (2, 2))
def test_default_float32_env(self):
box = Box(0.0, 1.0, (3, 4))
assert box.dtype == np.float32
box = Box(np.array([-1.0, -2.0]), np.array([1.0, 2.0]))
assert box.dtype == np.float32
def test_hash(self):
box = Box(0.0, 1.0, (3, 4))
assert box.__hash__() == 1213972508617964782