mirror of
https://github.com/Farama-Foundation/Gymnasium.git
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108f32c743
* gym.spaces.Tuple inherits from collections.abc.Sequence Following the PR I did a few months back (https://github.com/openai/gym/pull/2446), the tuple wrapper of gym should inherits from the abstract interface of Python. It is important for type check via `isinstance` and enable using such objects transparently with libraries such as [dmtree](https://github.com/deepmind/tree). It will bring a way helper methods along the way but it cannot be avoided to interoperability: : `__iter__`, `__reversed__`, `index`, and `count` Personally I don't think it is an issue since it is new features and it is not conflicting. As the previous PR, this patch is NOT removing any existing feature and should not break backward compatibility. * Add unit test * Fix unit tests * Final fix. * Remove irrelevant comment. * Fix black formatter.
618 lines
19 KiB
Python
618 lines
19 KiB
Python
import json # note: ujson fails this test due to float equality
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import copy
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import pickle
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import tempfile
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import numpy as np
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import pytest
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from gym.spaces import Tuple, Box, Discrete, MultiDiscrete, MultiBinary, Dict
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@pytest.mark.parametrize(
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"space",
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[
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Discrete(3),
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Discrete(5, start=-2),
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Box(low=0.0, high=np.inf, shape=(2, 2)),
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Tuple([Discrete(5), Discrete(10)]),
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Tuple(
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[
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Discrete(5),
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Box(low=np.array([0, 0]), high=np.array([1, 5]), dtype=np.float32),
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]
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),
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Tuple((Discrete(5), Discrete(2), Discrete(2))),
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Tuple((Discrete(5), Discrete(2, start=6), Discrete(2, start=-4))),
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MultiDiscrete([2, 2, 100]),
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MultiBinary(10),
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Dict(
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{
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"position": Discrete(5),
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"velocity": Box(
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low=np.array([0, 0]), high=np.array([1, 5]), dtype=np.float32
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),
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}
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),
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],
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)
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def test_roundtripping(space):
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sample_1 = space.sample()
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sample_2 = space.sample()
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assert space.contains(sample_1)
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assert space.contains(sample_2)
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json_rep = space.to_jsonable([sample_1, sample_2])
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json_roundtripped = json.loads(json.dumps(json_rep))
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samples_after_roundtrip = space.from_jsonable(json_roundtripped)
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sample_1_prime, sample_2_prime = samples_after_roundtrip
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s1 = space.to_jsonable([sample_1])
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s1p = space.to_jsonable([sample_1_prime])
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s2 = space.to_jsonable([sample_2])
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s2p = space.to_jsonable([sample_2_prime])
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assert s1 == s1p, f"Expected {s1} to equal {s1p}"
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assert s2 == s2p, f"Expected {s2} to equal {s2p}"
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@pytest.mark.parametrize(
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"space",
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[
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Discrete(3),
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Discrete(5, start=-2),
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Box(low=np.array([-10, 0]), high=np.array([10, 10]), dtype=np.float32),
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Box(low=-np.inf, high=np.inf, shape=(1, 3)),
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Tuple([Discrete(5), Discrete(10)]),
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Tuple(
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[
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Discrete(5),
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Box(low=np.array([0, 0]), high=np.array([1, 5]), dtype=np.float32),
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]
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),
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Tuple((Discrete(5), Discrete(2), Discrete(2))),
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Tuple((Discrete(5), Discrete(2), Discrete(2, start=-6))),
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MultiDiscrete([2, 2, 100]),
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MultiBinary(6),
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Dict(
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{
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"position": Discrete(5),
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"velocity": Box(
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low=np.array([0, 0]), high=np.array([1, 5]), dtype=np.float32
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),
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}
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),
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],
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)
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def test_equality(space):
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space1 = space
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space2 = copy.deepcopy(space)
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assert space1 == space2, f"Expected {space1} to equal {space2}"
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@pytest.mark.parametrize(
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"spaces",
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[
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(Discrete(3), Discrete(4)),
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(Discrete(3), Discrete(3, start=-1)),
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(MultiDiscrete([2, 2, 100]), MultiDiscrete([2, 2, 8])),
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(MultiBinary(8), MultiBinary(7)),
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(
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Box(low=np.array([-10, 0]), high=np.array([10, 10]), dtype=np.float32),
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Box(low=np.array([-10, 0]), high=np.array([10, 9]), dtype=np.float32),
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),
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(
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Box(low=-np.inf, high=0.0, shape=(2, 1)),
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Box(low=0.0, high=np.inf, shape=(2, 1)),
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),
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(Tuple([Discrete(5), Discrete(10)]), Tuple([Discrete(1), Discrete(10)])),
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(
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Tuple([Discrete(5), Discrete(10)]),
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Tuple([Discrete(5, start=7), Discrete(10)]),
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),
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(Dict({"position": Discrete(5)}), Dict({"position": Discrete(4)})),
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(Dict({"position": Discrete(5)}), Dict({"speed": Discrete(5)})),
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],
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)
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def test_inequality(spaces):
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space1, space2 = spaces
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assert space1 != space2, f"Expected {space1} != {space2}"
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@pytest.mark.parametrize(
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"space",
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[
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Discrete(5),
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Discrete(8, start=-20),
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Box(low=0, high=255, shape=(2,), dtype="uint8"),
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Box(low=-np.inf, high=np.inf, shape=(3, 3)),
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Box(low=1.0, high=np.inf, shape=(3, 3)),
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Box(low=-np.inf, high=2.0, shape=(3, 3)),
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],
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)
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def test_sample(space):
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space.seed(0)
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n_trials = 100
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samples = np.array([space.sample() for _ in range(n_trials)])
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expected_mean = 0.0
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if isinstance(space, Box):
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if space.is_bounded():
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expected_mean = (space.high + space.low) / 2
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elif space.is_bounded("below"):
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expected_mean = 1 + space.low
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elif space.is_bounded("above"):
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expected_mean = -1 + space.high
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else:
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expected_mean = 0.0
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elif isinstance(space, Discrete):
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expected_mean = space.start + space.n / 2
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else:
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raise NotImplementedError
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np.testing.assert_allclose(expected_mean, samples.mean(), atol=3.0 * samples.std())
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@pytest.mark.parametrize(
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"spaces",
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[
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(Discrete(5), MultiBinary(5)),
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(
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Box(low=np.array([-10, 0]), high=np.array([10, 10]), dtype=np.float32),
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MultiDiscrete([2, 2, 8]),
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),
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(
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Box(low=0, high=255, shape=(64, 64, 3), dtype=np.uint8),
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Box(low=0, high=255, shape=(32, 32, 3), dtype=np.uint8),
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),
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(Dict({"position": Discrete(5)}), Tuple([Discrete(5)])),
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(Dict({"position": Discrete(5)}), Discrete(5)),
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(Tuple((Discrete(5),)), Discrete(5)),
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(
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Box(low=np.array([-np.inf, 0.0]), high=np.array([0.0, np.inf])),
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Box(low=np.array([-np.inf, 1.0]), high=np.array([0.0, np.inf])),
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),
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],
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)
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def test_class_inequality(spaces):
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assert spaces[0] == spaces[0]
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assert spaces[1] == spaces[1]
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assert spaces[0] != spaces[1]
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assert spaces[1] != spaces[0]
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@pytest.mark.parametrize(
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"space_fn",
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[
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lambda: Dict(space1="abc"),
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lambda: Dict({"space1": "abc"}),
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lambda: Tuple(["abc"]),
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],
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)
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def test_bad_space_calls(space_fn):
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with pytest.raises(AssertionError):
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space_fn()
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def test_seed_Dict():
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test_space = Dict(
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{
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"a": Box(low=0, high=1, shape=(3, 3)),
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"b": Dict(
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{
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"b_1": Box(low=-100, high=100, shape=(2,)),
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"b_2": Box(low=-1, high=1, shape=(2,)),
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}
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),
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"c": Discrete(5),
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}
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)
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seed_dict = {
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"a": 0,
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"b": {
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"b_1": 1,
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"b_2": 2,
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},
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"c": 3,
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}
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test_space.seed(seed_dict)
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# "Unpack" the dict sub-spaces into individual spaces
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a = Box(low=0, high=1, shape=(3, 3))
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a.seed(0)
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b_1 = Box(low=-100, high=100, shape=(2,))
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b_1.seed(1)
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b_2 = Box(low=-1, high=1, shape=(2,))
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b_2.seed(2)
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c = Discrete(5)
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c.seed(3)
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for i in range(10):
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test_s = test_space.sample()
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a_s = a.sample()
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assert (test_s["a"] == a_s).all()
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b_1_s = b_1.sample()
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assert (test_s["b"]["b_1"] == b_1_s).all()
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b_2_s = b_2.sample()
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assert (test_s["b"]["b_2"] == b_2_s).all()
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c_s = c.sample()
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assert test_s["c"] == c_s
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def test_box_dtype_check():
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# Related Issues:
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# https://github.com/openai/gym/issues/2357
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# https://github.com/openai/gym/issues/2298
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space = Box(0, 2, tuple(), dtype=np.float32)
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# casting will match the correct type
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assert space.contains(0.5)
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# float64 is not in float32 space
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assert not space.contains(np.array(0.5))
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assert not space.contains(np.array(1))
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@pytest.mark.parametrize(
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"space",
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[
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Discrete(3),
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Discrete(3, start=-4),
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Box(low=0.0, high=np.inf, shape=(2, 2)),
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Tuple([Discrete(5), Discrete(10)]),
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Tuple(
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[
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Discrete(5),
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Box(low=np.array([0, 0]), high=np.array([1, 5]), dtype=np.float32),
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]
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),
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Tuple((Discrete(5), Discrete(2), Discrete(2))),
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MultiDiscrete([2, 2, 100]),
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MultiBinary(10),
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Dict(
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{
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"position": Discrete(5),
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"velocity": Box(
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low=np.array([0, 0]), high=np.array([1, 5]), dtype=np.float32
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),
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}
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),
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],
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)
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def test_seed_returns_list(space):
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def assert_integer_list(seed):
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assert isinstance(seed, list)
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assert len(seed) >= 1
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assert all([isinstance(s, int) for s in seed])
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assert_integer_list(space.seed(None))
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assert_integer_list(space.seed(0))
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def convert_sample_hashable(sample):
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if isinstance(sample, np.ndarray):
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return tuple(sample.tolist())
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if isinstance(sample, (list, tuple)):
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return tuple(convert_sample_hashable(s) for s in sample)
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if isinstance(sample, dict):
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return tuple(
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(key, convert_sample_hashable(value)) for key, value in sample.items()
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)
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return sample
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def sample_equal(sample1, sample2):
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return convert_sample_hashable(sample1) == convert_sample_hashable(sample2)
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@pytest.mark.parametrize(
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"space",
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[
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Discrete(3),
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Discrete(3, start=-4),
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Box(low=0.0, high=np.inf, shape=(2, 2)),
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Tuple([Discrete(5), Discrete(10)]),
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Tuple(
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[
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Discrete(5),
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Box(low=np.array([0, 0]), high=np.array([1, 5]), dtype=np.float32),
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]
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),
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Tuple((Discrete(5), Discrete(2), Discrete(2))),
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MultiDiscrete([2, 2, 100]),
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MultiBinary(10),
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Dict(
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{
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"position": Discrete(5),
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"velocity": Box(
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low=np.array([0, 0]), high=np.array([1, 5]), dtype=np.float32
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),
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}
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),
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],
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)
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def test_seed_reproducibility(space):
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space1 = space
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space2 = copy.deepcopy(space)
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space1.seed(None)
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space2.seed(None)
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assert space1.seed(0) == space2.seed(0)
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assert sample_equal(space1.sample(), space2.sample())
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@pytest.mark.parametrize(
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"space",
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[
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Tuple([Discrete(100), Discrete(100)]),
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Tuple([Discrete(5), Discrete(10)]),
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Tuple([Discrete(5), Discrete(5, start=10)]),
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Tuple(
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[
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Discrete(5),
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Box(low=np.array([0, 0]), high=np.array([1, 5]), dtype=np.float32),
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]
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),
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Tuple((Discrete(5), Discrete(2), Discrete(2))),
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Dict(
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{
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"position": Discrete(5),
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"velocity": Box(
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low=np.array([0, 0]), high=np.array([1, 5]), dtype=np.float32
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),
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}
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),
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],
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)
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def test_seed_subspace_incorrelated(space):
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subspaces = space.spaces if isinstance(space, Tuple) else space.spaces.values()
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space.seed(0)
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states = [
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convert_sample_hashable(subspace.np_random.bit_generator.state)
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for subspace in subspaces
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]
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assert len(states) == len(set(states))
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def test_tuple():
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spaces = [Discrete(5), Discrete(10), Discrete(5)]
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space_tuple = Tuple(spaces)
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assert len(space_tuple) == len(spaces)
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assert space_tuple.count(Discrete(5)) == 2
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assert space_tuple.count(MultiBinary(2)) == 0
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for i, space in enumerate(space_tuple):
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assert space == spaces[i]
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for i, space in enumerate(reversed(space_tuple)):
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assert space == spaces[len(spaces) - 1 - i]
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assert space_tuple.index(Discrete(5)) == 0
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assert space_tuple.index(Discrete(5), 1) == 2
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with pytest.raises(ValueError):
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space_tuple.index(Discrete(10), 0, 1)
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def test_multidiscrete_as_tuple():
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# 1D multi-discrete
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space = MultiDiscrete([3, 4, 5])
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assert space.shape == (3,)
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assert space[0] == Discrete(3)
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assert space[0:1] == MultiDiscrete([3])
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assert space[0:2] == MultiDiscrete([3, 4])
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assert space[:] == space and space[:] is not space
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assert len(space) == 3
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# 2D multi-discrete
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space = MultiDiscrete([[3, 4, 5], [6, 7, 8]])
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assert space.shape == (2, 3)
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assert space[0, 1] == Discrete(4)
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assert space[0] == MultiDiscrete([3, 4, 5])
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assert space[0:1] == MultiDiscrete([[3, 4, 5]])
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assert space[0:2, :] == MultiDiscrete([[3, 4, 5], [6, 7, 8]])
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assert space[:, 0:1] == MultiDiscrete([[3], [6]])
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assert space[0:2, 0:2] == MultiDiscrete([[3, 4], [6, 7]])
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assert space[:] == space and space[:] is not space
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assert space[:, :] == space and space[:, :] is not space
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def test_multidiscrete_subspace_reproducibility():
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# 1D multi-discrete
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space = MultiDiscrete([100, 200, 300])
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space.seed(None)
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assert sample_equal(space[0].sample(), space[0].sample())
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assert sample_equal(space[0:1].sample(), space[0:1].sample())
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assert sample_equal(space[0:2].sample(), space[0:2].sample())
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assert sample_equal(space[:].sample(), space[:].sample())
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assert sample_equal(space[:].sample(), space.sample())
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# 2D multi-discrete
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space = MultiDiscrete([[300, 400, 500], [600, 700, 800]])
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space.seed(None)
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assert sample_equal(space[0, 1].sample(), space[0, 1].sample())
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assert sample_equal(space[0].sample(), space[0].sample())
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assert sample_equal(space[0:1].sample(), space[0:1].sample())
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assert sample_equal(space[0:2, :].sample(), space[0:2, :].sample())
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assert sample_equal(space[:, 0:1].sample(), space[:, 0:1].sample())
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assert sample_equal(space[0:2, 0:2].sample(), space[0:2, 0:2].sample())
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assert sample_equal(space[:].sample(), space[:].sample())
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assert sample_equal(space[:, :].sample(), space[:, :].sample())
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assert sample_equal(space[:, :].sample(), space.sample())
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def test_space_legacy_state_pickling():
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legacy_state = {
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"shape": (
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1,
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2,
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3,
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),
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"dtype": np.int64,
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"np_random": np.random.default_rng(),
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"n": 3,
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}
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space = Discrete(1)
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space.__setstate__(legacy_state)
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assert space.shape == legacy_state["shape"]
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assert space._shape == legacy_state["shape"]
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assert space.np_random == legacy_state["np_random"]
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assert space._np_random == legacy_state["np_random"]
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assert space.n == 3
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assert space.dtype == legacy_state["dtype"]
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@pytest.mark.parametrize(
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"space",
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[
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Box(low=0, high=np.inf, shape=(2,), dtype=np.int32),
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Box(low=0, high=np.inf, shape=(2,), dtype=np.float32),
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Box(low=0, high=np.inf, shape=(2,), dtype=np.int64),
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Box(low=0, high=np.inf, shape=(2,), dtype=np.float64),
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Box(low=-np.inf, high=0, shape=(2,), dtype=np.int32),
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Box(low=-np.inf, high=0, shape=(2,), dtype=np.float32),
|
|
Box(low=-np.inf, high=0, shape=(2,), dtype=np.int64),
|
|
Box(low=-np.inf, high=0, shape=(2,), dtype=np.float64),
|
|
Box(low=-np.inf, high=np.inf, shape=(2,), dtype=np.int32),
|
|
Box(low=-np.inf, high=np.inf, shape=(2,), dtype=np.float32),
|
|
Box(low=-np.inf, high=np.inf, shape=(2,), dtype=np.int64),
|
|
Box(low=-np.inf, high=np.inf, shape=(2,), dtype=np.float64),
|
|
Box(low=0, high=np.inf, shape=(2, 3), dtype=np.int32),
|
|
Box(low=0, high=np.inf, shape=(2, 3), dtype=np.float32),
|
|
Box(low=0, high=np.inf, shape=(2, 3), dtype=np.int64),
|
|
Box(low=0, high=np.inf, shape=(2, 3), dtype=np.float64),
|
|
Box(low=-np.inf, high=0, shape=(2, 3), dtype=np.int32),
|
|
Box(low=-np.inf, high=0, shape=(2, 3), dtype=np.float32),
|
|
Box(low=-np.inf, high=0, shape=(2, 3), dtype=np.int64),
|
|
Box(low=-np.inf, high=0, shape=(2, 3), dtype=np.float64),
|
|
Box(low=-np.inf, high=np.inf, shape=(2, 3), dtype=np.int32),
|
|
Box(low=-np.inf, high=np.inf, shape=(2, 3), dtype=np.float32),
|
|
Box(low=-np.inf, high=np.inf, shape=(2, 3), dtype=np.int64),
|
|
Box(low=-np.inf, high=np.inf, shape=(2, 3), dtype=np.float64),
|
|
Box(low=np.array([-np.inf, 0]), high=np.array([0.0, np.inf]), dtype=np.int32),
|
|
Box(low=np.array([-np.inf, 0]), high=np.array([0.0, np.inf]), dtype=np.float32),
|
|
Box(low=np.array([-np.inf, 0]), high=np.array([0.0, np.inf]), dtype=np.int64),
|
|
Box(low=np.array([-np.inf, 0]), high=np.array([0.0, np.inf]), dtype=np.float64),
|
|
],
|
|
)
|
|
def test_infinite_space(space):
|
|
# for this test, make sure that spaces that are passed in have only 0 or infinite bounds
|
|
# because space.high and space.low are both modified within the init
|
|
# so we check for infinite when we know it's not 0
|
|
space.seed(0)
|
|
|
|
assert np.all(space.high > space.low), "High bound not higher than low bound"
|
|
|
|
sample = space.sample()
|
|
|
|
# check if space contains sample
|
|
assert space.contains(
|
|
sample
|
|
), "Sample {sample} not inside space according to `space.contains()`"
|
|
|
|
# manually check that the sign of the sample is within the bounds
|
|
assert np.all(
|
|
np.sign(space.high) >= np.sign(sample)
|
|
), f"Sign of sample {sample} is less than space upper bound {space.high}"
|
|
assert np.all(
|
|
np.sign(space.low) <= np.sign(sample)
|
|
), f"Sign of sample {sample} is more than space lower bound {space.low}"
|
|
|
|
# check that int bounds are bounded for everything
|
|
# but floats are unbounded for infinite
|
|
if np.any(space.high != 0):
|
|
assert (
|
|
space.is_bounded("above") == False
|
|
), "inf upper bound supposed to be unbounded"
|
|
else:
|
|
assert (
|
|
space.is_bounded("above") == True
|
|
), "non-inf upper bound supposed to be bounded"
|
|
|
|
if np.any(space.low != 0):
|
|
assert (
|
|
space.is_bounded("below") == False
|
|
), "inf lower bound supposed to be unbounded"
|
|
else:
|
|
assert (
|
|
space.is_bounded("below") == True
|
|
), "non-inf lower bound supposed to be bounded"
|
|
|
|
# check for dtype
|
|
assert (
|
|
space.high.dtype == space.dtype
|
|
), "High's dtype {space.high.dtype} doesn't match `space.dtype`'"
|
|
assert (
|
|
space.low.dtype == space.dtype
|
|
), "Low's dtype {space.high.dtype} doesn't match `space.dtype`'"
|
|
|
|
|
|
def test_discrete_legacy_state_pickling():
|
|
legacy_state = {
|
|
"n": 3,
|
|
}
|
|
|
|
d = Discrete(1)
|
|
assert "start" in d.__dict__
|
|
del d.__dict__["start"] # legacy did not include start param
|
|
assert "start" not in d.__dict__
|
|
|
|
d.__setstate__(legacy_state)
|
|
|
|
assert d.start == 0
|
|
assert d.n == 3
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
"space",
|
|
[
|
|
Discrete(3),
|
|
Discrete(5, start=-2),
|
|
Box(low=0.0, 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))),
|
|
Tuple((Discrete(5), Discrete(2, start=6), Discrete(2, start=-4))),
|
|
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_pickle(space):
|
|
space.sample()
|
|
|
|
# Pickle and unpickle with a string
|
|
pickled = pickle.dumps(space)
|
|
space2 = pickle.loads(pickled)
|
|
|
|
# Pickle and unpickle with a file
|
|
with tempfile.TemporaryFile() as f:
|
|
pickle.dump(space, f)
|
|
f.seek(0)
|
|
space3 = pickle.load(f)
|
|
|
|
sample = space.sample()
|
|
sample2 = space2.sample()
|
|
sample3 = space3.sample()
|
|
assert sample_equal(sample, sample2)
|
|
assert sample_equal(sample, sample3)
|