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_convert_tf(self):
d = Dict({'position': Discrete(2), 'velocity': Discrete(3)})
tensor_dict = d.to_tf_placeholder('test', 1)
assert isinstance(tensor_dict, Dict)
assert all(
[isinstance(c, tf.Tensor) for c in tensor_dict.spaces.values()])
assert all([v.dtype == tf.int64 for v in tensor_dict.spaces.values()])
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_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_convert_theano(self):
d = Dict({'position': Discrete(2), 'velocity': Discrete(3)})
tensor_dict = d.to_theano_tensor('test', 1)
assert isinstance(tensor_dict, Dict)
assert all([
isinstance(c, theano.tensor.TensorVariable)
for c in tensor_dict.spaces.values()
])
assert all(
[space.dtype == 'int64' for space in tensor_dict.spaces.values()])
def test_pickleable(self):
motion_dict = {'position': Discrete(2), 'velocity': Discrete(3)}
sample = {
'position': 1,
'velocity': 2,
}
d = Dict(motion_dict)
round_trip = pickle.loads(pickle.dumps(d))
assert d.contains(sample)
assert round_trip
assert round_trip.contains(sample)
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 _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_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_flat_dim(self):
d = Dict(
collections.OrderedDict(position=Box(0, 10, (2, )),
velocity=Box(0, 10, (3, ))))
assert d.flat_dim == 5