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
), f"Expected sample #{i} {original} to equal {roundtripped}"
def step(self, actions):
# Make sure actions is a list
if isinstance(actions, np.ndarray):
actions = actions.tolist()
elif not isinstance(actions, list):
actions = [actions]
req = api_v1.StepRequest(instanceId=self.instanceId, actions=actions)
res = self.client.Step(req)
reward = res.reward
done = res.isDone
obs = gym_utils.unflatten(self.observation_space, res.observation)
info = {} # @TODO, convert Protobuf map<string, string> to Dict
return [obs, reward, done, info]
def test_dtypes(original_space, expected_flattened_dtype):
flattened_space = utils.flatten_space(original_space)
original_sample = original_space.sample()
flattened_sample = utils.flatten(original_space, original_sample)
unflattened_sample = utils.unflatten(original_space, flattened_sample)
assert flattened_space.contains(
flattened_sample
), "Expected flattened_space to contain flattened_sample"
assert flattened_space.dtype == expected_flattened_dtype, "Expected flattened_space's dtype to equal " \
"{}".format(expected_flattened_dtype)
assert flattened_sample.dtype == flattened_space.dtype, "Expected flattened_space's dtype to equal " \
"flattened_sample's dtype "
compare_sample_types(original_space, original_sample, unflattened_sample)
def test_unflatten(space, flattened_sample, expected_sample):
sample = utils.unflatten(space, flattened_sample)
assert compare_nested(sample, expected_sample)