def test_tensor_spec_from_gym_space_dict(self):
dict_space = gym.spaces.Dict([
('spec_2', gym.spaces.Box(-1.0, 1.0, (3, 4))),
('spec_1', gym.spaces.Discrete(2)),
])
spec = alf_gym_wrapper.tensor_spec_from_gym_space(dict_space)
keys = list(spec.keys())
self.assertEqual('spec_1', keys[1])
self.assertEqual(2, len(spec))
self.assertEqual((), spec['spec_1'].shape)
self.assertEqual(torch.int64, spec['spec_1'].dtype)
self.assertEqual(0, spec['spec_1'].minimum)
self.assertEqual(1, spec['spec_1'].maximum)
self.assertEqual('spec_2', keys[0])
self.assertEqual((3, 4), spec['spec_2'].shape)
self.assertEqual(torch.float32, spec['spec_2'].dtype)
np.testing.assert_array_almost_equal(
-np.ones((3, 4)),
spec['spec_2'].minimum,
)
np.testing.assert_array_almost_equal(
np.ones((3, 4)),
spec['spec_2'].maximum,
)
def test_tensor_spec_from_gym_space_discrete(self):
discrete_space = gym.spaces.Discrete(3)
spec = alf_gym_wrapper.tensor_spec_from_gym_space(discrete_space)
self.assertEqual((), spec.shape)
self.assertEqual(torch.int64, spec.dtype)
self.assertEqual(0, spec.minimum)
self.assertEqual(2, spec.maximum)
def test_tensor_spec_from_gym_space_tuple_mixed(self):
tuple_space = gym.spaces.Tuple((
gym.spaces.Discrete(2),
gym.spaces.Box(-1.0, 1.0, (3, 4)),
gym.spaces.Tuple((gym.spaces.Discrete(2), gym.spaces.Discrete(3))),
gym.spaces.Dict({
'spec_1':
gym.spaces.Discrete(2),
'spec_2':
gym.spaces.Tuple(
(gym.spaces.Discrete(2), gym.spaces.Discrete(3))),
}),
))
spec = alf_gym_wrapper.tensor_spec_from_gym_space(tuple_space)
self.assertEqual(4, len(spec))
# Test Discrete
self.assertEqual((), spec[0].shape)
self.assertEqual(torch.int64, spec[0].dtype)
self.assertEqual(0, spec[0].minimum)
self.assertEqual(1, spec[0].maximum)
# Test Box
self.assertEqual((3, 4), spec[1].shape)
self.assertEqual(torch.float32, spec[1].dtype)
np.testing.assert_array_almost_equal(-np.ones((3, 4)), spec[1].minimum)
np.testing.assert_array_almost_equal(np.ones((3, 4)), spec[1].maximum)
# Test Tuple
self.assertEqual(2, len(spec[2]))
self.assertEqual((), spec[2][0].shape)
self.assertEqual(torch.int64, spec[2][0].dtype)
self.assertEqual(0, spec[2][0].minimum)
self.assertEqual(1, spec[2][0].maximum)
self.assertEqual((), spec[2][1].shape)
self.assertEqual(torch.int64, spec[2][1].dtype)
self.assertEqual(0, spec[2][1].minimum)
self.assertEqual(2, spec[2][1].maximum)
# Test Dict
# Test Discrete in Dict
discrete_in_dict = spec[3]['spec_1']
self.assertEqual((), discrete_in_dict.shape)
self.assertEqual(torch.int64, discrete_in_dict.dtype)
self.assertEqual(0, discrete_in_dict.minimum)
self.assertEqual(1, discrete_in_dict.maximum)
# Test Tuple in Dict
tuple_in_dict = spec[3]['spec_2']
self.assertEqual(2, len(tuple_in_dict))
self.assertEqual((), tuple_in_dict[0].shape)
self.assertEqual(torch.int64, tuple_in_dict[0].dtype)
self.assertEqual(0, tuple_in_dict[0].minimum)
self.assertEqual(1, tuple_in_dict[0].maximum)
self.assertEqual((), tuple_in_dict[1].shape)
self.assertEqual(torch.int64, tuple_in_dict[1].dtype)
self.assertEqual(0, tuple_in_dict[1].minimum)
self.assertEqual(2, tuple_in_dict[1].maximum)
def test_tensor_spec_from_gym_space_box_scalars(self):
for dtype in (np.float32, np.float64):
box_space = gym.spaces.Box(-1.0, 1.0, (3, 4), dtype=dtype)
spec = alf_gym_wrapper.tensor_spec_from_gym_space(box_space)
torch_dtype = getattr(torch, np.dtype(dtype).name)
self.assertEqual((3, 4), spec.shape)
self.assertEqual(torch_dtype, spec.dtype)
np.testing.assert_array_equal(-np.ones((3, 4)), spec.minimum)
np.testing.assert_array_equal(np.ones((3, 4)), spec.maximum)
def test_tensor_spec_from_gym_space_multi_binary(self):
multi_binary_space = gym.spaces.MultiBinary(4)
spec = alf_gym_wrapper.tensor_spec_from_gym_space(multi_binary_space)
self.assertEqual((4, ), spec.shape)
self.assertEqual(torch.int8, spec.dtype)
np.testing.assert_array_equal(np.array([0], dtype=np.int),
spec.minimum)
np.testing.assert_array_equal(np.array([1], dtype=np.int),
spec.maximum)
def test_tensor_spec_from_gym_space_multi_discrete(self):
multi_discrete_space = gym.spaces.MultiDiscrete([1, 2, 3, 4])
spec = alf_gym_wrapper.tensor_spec_from_gym_space(multi_discrete_space)
self.assertEqual((4, ), spec.shape)
self.assertEqual(torch.int64, spec.dtype)
np.testing.assert_array_equal(np.array([0], dtype=np.int),
spec.minimum)
np.testing.assert_array_equal(np.array([0, 1, 2, 3], dtype=np.int),
spec.maximum)
def test_tensor_spec_from_gym_space_box_scalars_simplify_bounds(self):
box_space = gym.spaces.Box(-1.0, 1.0, (3, 4))
spec = alf_gym_wrapper.tensor_spec_from_gym_space(
box_space, simplify_box_bounds=True)
self.assertEqual((3, 4), spec.shape)
self.assertEqual(torch.float32, spec.dtype)
np.testing.assert_array_equal(np.array([-1], dtype=np.int),
spec.minimum)
np.testing.assert_array_equal(np.array([1], dtype=np.int),
spec.maximum)
def test_tensor_spec_from_gym_space_box_array(self):
for dtype in (np.float32, np.float64):
box_space = gym.spaces.Box(np.array([-1.0, -2.0]),
np.array([2.0, 4.0]),
dtype=dtype)
spec = alf_gym_wrapper.tensor_spec_from_gym_space(box_space)
torch_dtype = getattr(torch, np.dtype(dtype).name)
self.assertEqual((2, ), spec.shape)
self.assertEqual(torch_dtype, spec.dtype)
np.testing.assert_array_equal(np.array([-1.0, -2.0]), spec.minimum)
np.testing.assert_array_equal(np.array([2.0, 4.0]), spec.maximum)
def test_tensor_spec_from_gym_space_tuple(self):
tuple_space = gym.spaces.Tuple(
(gym.spaces.Discrete(2), gym.spaces.Discrete(3)))
spec = alf_gym_wrapper.tensor_spec_from_gym_space(tuple_space)
self.assertEqual(2, len(spec))
self.assertEqual((), spec[0].shape)
self.assertEqual(torch.int64, spec[0].dtype)
self.assertEqual(0, spec[0].minimum)
self.assertEqual(1, spec[0].maximum)
self.assertEqual((), spec[1].shape)
self.assertEqual(torch.int64, spec[1].dtype)
self.assertEqual(0, spec[1].minimum)
self.assertEqual(2, spec[1].maximum)
def test_tensor_spec_from_gym_space_when_simplify_box_bounds_false(self):
# testing on gym.spaces.Dict which makes recursive calls to
# _tensor_spec_from_gym_space
box_space = gym.spaces.Box(-1.0, 1.0, (2, ))
dict_space = gym.spaces.Dict({'box1': box_space, 'box2': box_space})
spec = alf_gym_wrapper.tensor_spec_from_gym_space(
dict_space, simplify_box_bounds=False)
self.assertEqual((2, ), spec['box1'].shape)
self.assertEqual((2, ), spec['box2'].shape)
self.assertEqual(torch.float32, spec['box1'].dtype)
self.assertEqual(torch.float32, spec['box2'].dtype)
np.testing.assert_array_equal(np.array([-1, -1], dtype=np.int),
spec['box1'].minimum)
np.testing.assert_array_equal(np.array([1, 1], dtype=np.int),
spec['box1'].maximum)
np.testing.assert_array_equal(np.array([-1, -1], dtype=np.int),
spec['box2'].minimum)
np.testing.assert_array_equal(np.array([1, 1], dtype=np.int),
spec['box2'].maximum)
