Improve observation space of FlattenObservation wrapper (#1884)

* Add tests for gym.spaces.utils.

* Add docstrings to gym.spaces.utils.

* Remove some trailing whitespace.

* Add gym.spaces.utils.flatten_space.

The new function also is reexported as gym.spaces.flatten_space. It
improves the determination of observation_space in
gym.wrappers.FlattenObservation.

* Produce OrderedDict instead of dict in gym.spaces.unflatten().

`gym.spaces.Dict` is very particular about producing its samples as
`OrderedDict` in order preserve the order of its items. Hence,
`unflatten()` should reproduce this behavior.

* In test_utils.compare_nested, also verify order of OrderedDict items.

* Add examples to flatten_space() docstring.

* Document ``flatten(space, space.sample()) in flatten_space(space)``.

Co-authored-by: Nico Madysa <nico.madysa@tu-dresden.de>

gym/spaces/tests/test_utils.py

@@ -0,0 +1,120 @@
+from collections import OrderedDict
+import numpy as np
+import pytest
+
+from gym.spaces import utils
+from gym.spaces import Tuple, Box, Discrete, MultiDiscrete, MultiBinary, Dict
+
+
+@pytest.mark.parametrize(["space", "flatdim"], [
+    (Discrete(3), 3),
+    (Box(low=0., high=np.inf, shape=(2, 2)), 4),
+    (Tuple([Discrete(5), Discrete(10)]), 15),
+    (Tuple([Discrete(5), Box(low=np.array([0, 0]), high=np.array([1, 5]), dtype=np.float32)]), 7),
+    (Tuple((Discrete(5), Discrete(2), Discrete(2))), 9),
+    (MultiDiscrete([2, 2, 100]), 3),
+    (MultiBinary(10), 10),
+    (Dict({"position": Discrete(5),
+           "velocity": Box(low=np.array([0, 0]), high=np.array([1, 5]), dtype=np.float32)}), 7),
+])
+def test_flatdim(space, flatdim):
+    dim = utils.flatdim(space)
+    assert dim == flatdim, "Expected {} to equal {}".format(dim, flatdim)
+
+
+@pytest.mark.parametrize("space", [
+    Discrete(3),
+    Box(low=0., high=np.inf, shape=(2, 2)),
+    Tuple([Discrete(5), Discrete(10)]),
+    Tuple([Discrete(5), Box(low=np.array([0, 0]), high=np.array([1, 5]), dtype=np.float32)]),
+    Tuple((Discrete(5), Discrete(2), Discrete(2))),
+    MultiDiscrete([2, 2, 100]),
+    MultiBinary(10),
+    Dict({"position": Discrete(5),
+          "velocity": Box(low=np.array([0, 0]), high=np.array([1, 5]), dtype=np.float32)}),
+    ])
+def test_flatten_space_boxes(space):
+    flat_space = utils.flatten_space(space)
+    assert isinstance(flat_space, Box), "Expected {} to equal {}".format(type(flat_space), Box)
+    flatdim = utils.flatdim(space)
+    (single_dim, ) = flat_space.shape
+    assert single_dim == flatdim, "Expected {} to equal {}".format(single_dim, flatdim)
+
+
+@pytest.mark.parametrize("space", [
+    Discrete(3),
+    Box(low=0., high=np.inf, shape=(2, 2)),
+    Tuple([Discrete(5), Discrete(10)]),
+    Tuple([Discrete(5), Box(low=np.array([0, 0]), high=np.array([1, 5]), dtype=np.float32)]),
+    Tuple((Discrete(5), Discrete(2), Discrete(2))),
+    MultiDiscrete([2, 2, 100]),
+    MultiBinary(10),
+    Dict({"position": Discrete(5),
+          "velocity": Box(low=np.array([0, 0]), high=np.array([1, 5]), dtype=np.float32)}),
+    ])
+def test_flat_space_contains_flat_points(space):
+    some_samples = [space.sample() for _ in range(10)]
+    flattened_samples = [utils.flatten(space, sample) for sample in some_samples]
+    flat_space = utils.flatten_space(space)
+    for i, flat_sample in enumerate(flattened_samples):
+        assert flat_sample in flat_space,\
+            'Expected sample #{} {} to be in {}'.format(i, flat_sample, flat_space)
+
+
+@pytest.mark.parametrize("space", [
+    Discrete(3),
+    Box(low=0., high=np.inf, shape=(2, 2)),
+    Tuple([Discrete(5), Discrete(10)]),
+    Tuple([Discrete(5), Box(low=np.array([0, 0]), high=np.array([1, 5]), dtype=np.float32)]),
+    Tuple((Discrete(5), Discrete(2), Discrete(2))),
+    MultiDiscrete([2, 2, 100]),
+    MultiBinary(10),
+    Dict({"position": Discrete(5),
+          "velocity": Box(low=np.array([0, 0]), high=np.array([1, 5]), dtype=np.float32)}),
+    ])
+def test_flatten_dim(space):
+    sample = utils.flatten(space, space.sample())
+    (single_dim, ) = sample.shape
+    flatdim = utils.flatdim(space)
+    assert single_dim == flatdim, "Expected {} to equal {}".format(single_dim, flatdim)
+
+
+@pytest.mark.parametrize("space", [
+    Discrete(3),
+    Box(low=0., high=np.inf, shape=(2, 2)),
+    Tuple([Discrete(5), Discrete(10)]),
+    Tuple([Discrete(5), Box(low=np.array([0, 0]), high=np.array([1, 5]), dtype=np.float32)]),
+    Tuple((Discrete(5), Discrete(2), Discrete(2))),
+    MultiDiscrete([2, 2, 100]),
+    MultiBinary(10),
+    Dict({"position": Discrete(5),
+          "velocity": Box(low=np.array([0, 0]), high=np.array([1, 5]), dtype=np.float32)}),
+])
+def test_flatten_roundtripping(space):
+    some_samples = [space.sample() for _ in range(10)]
+    flattened_samples = [utils.flatten(space, sample) for sample in some_samples]
+    roundtripped_samples = [utils.unflatten(space, sample) for sample in flattened_samples]
+    for i, (original, roundtripped) in enumerate(zip(some_samples, roundtripped_samples)):
+        assert compare_nested(original, roundtripped), \
+            'Expected sample #{} {} to equal {}'.format(i, original, roundtripped)
+
+
+def compare_nested(left, right):
+    if isinstance(left, np.ndarray) and isinstance(right, np.ndarray):
+        return np.allclose(left, right)
+    elif isinstance(left, OrderedDict) and isinstance(right, OrderedDict):
+        res = len(left) == len(right)
+        for ((left_key, left_value), (right_key, right_value)) in zip(left.items(), right.items()):
+            if not res:
+                return False
+            res = left_key == right_key and compare_nested(left_value, right_value)
+        return res
+    elif isinstance(left, (tuple, list)) and isinstance(right, (tuple, list)):
+        res = len(left) == len(right)
+        for (x, y) in zip(left, right):
+            if not res:
+                return False
+            res = compare_nested(x, y)
+        return res
+    else:
+        return left == right