Import hangs with Gym >0.21

[callumtilbury]

When installing lb-foraging with certain versions of gym, the import hangs for a (very) long time.

Consider a simple script, import_tests.py:

from time import time
start = time()
import lbforaging
print(f"time = {time() - start:.3f}s")

With gym==0.21.*:

$ python import_tests.py
time = 0.650s

vs. with gym==0.22.*:

$ python import_tests.py
time = 1188.446s

This massive bottleneck is caused by the way gym registers environments from v0.22 onwards. Consider the gym/envs/registrations.py::EnvRegistry::register() method in v0.21:

https://github.com/openai/gym/blob/c755d5c35a25ab118746e2ba885894ff66fb8c43/gym/envs/registration.py#L205-L217

    def register(self, id, **kwargs):
        if self._ns is not None:
            if "/" in id:
                namespace, id = id.split("/")
                logger.warn(
                    f"Custom namespace '{namespace}' is being overrode by namespace '{self._ns}'. "
                    "If you are developing a plugin you shouldn't specify a namespace in `register` calls. "
                    "The namespace is specified through the entry point key."
                )
            id = f"{self._ns}/{id}"
        if id in self.env_specs:
            logger.warn("Overriding environment {}".format(id))
        self.env_specs[id] = EnvSpec(id, **kwargs)

See how this is different in v0.22:

https://github.com/openai/gym/blob/95063a08943e1f587c58be7435d94f81ccac8fd9/gym/envs/registration.py#L542-L596

    def register(self, id: str, **kwargs) -> None:
        spec = EnvSpec(id, **kwargs)


        if self._ns is not None:
            if spec.namespace is not None:
                logger.warn(
                    f"Custom namespace `{spec.namespace}` is being overridden "
                    f"by namespace `{self._ns}`. If you are developing a "
                    "plugin you shouldn't specify a namespace in `register` "
                    "calls. The namespace is specified through the "
                    "entry point package metadata."
                )
            # Replace namespace
            spec.namespace = self._ns


        try:
            # Get all versions of this spec.
            versions = self.env_specs.versions(spec.namespace, spec.name)


            # We raise an error if the user is attempting to initialize an
            # unversioned environment when a versioned one already exists.
            latest_versioned_spec = max(
                filter(lambda spec: isinstance(spec.version, int), versions),
                key=lambda spec: cast(int, spec.version),
                default=None,
            )
            unversioned_spec = next(
                filter(lambda spec: spec.version is None, versions), None
            )


            # Trying to register an unversioned spec when versioned spec exists
            if unversioned_spec and spec.version is not None:
                message = (
                    "Can't register the versioned environment "
                    f"`{spec.id}` when the unversioned environment "
                    f"`{unversioned_spec.id}` of the same name already exists."
                )
                raise error.RegistrationError(message)
            elif latest_versioned_spec and spec.version is None:
                message = (
                    f"Can't register the unversioned environment `{spec.id}` "
                    f"when version `{latest_versioned_spec.version}` "
                    "of the same name already exists. Note: the default "
                    "behavior is that the `gym.make` with the unversioned "
                    "environment will return the latest versioned environment."
                )
                raise error.RegistrationError(message)
        # We might not find this namespace or name in which case
        # we should continue to register the environment.
        except (error.NamespaceNotFound, error.NameNotFound):
            pass
        finally:
            if spec.id in self.env_specs:
                logger.warn(f"Overriding environment {id}")
            self.env_specs[spec.id] = spec

Specifically, the issue here is the addition of:
https://github.com/openai/gym/blob/95063a08943e1f587c58be7435d94f81ccac8fd9/gym/envs/registration.py#L559

            versions = self.env_specs.versions(spec.namespace, spec.name)

which calls
https://github.com/openai/gym/blob/95063a08943e1f587c58be7435d94f81ccac8fd9/gym/envs/registration.py#L220

        self._assert_name_exists(namespace, name)

which eventually hits:
https://github.com/openai/gym/blob/95063a08943e1f587c58be7435d94f81ccac8fd9/gym/envs/registration.py#L293

            suggestions = difflib.get_close_matches(name, self.names(namespace), n=1)

Herein lies the problem: lbforaging attempts to register a large number of environments (9720 by default). With each new environment, gym checks whether any environment has already been registered with a similar name. Naturally, this fuzzy match scales really badly with a large number of environments being registered.

v0.23 of gym takes the same approach to registration as v0.22, and thus faces the same issue. In v0.24-v0.26, the registration no longer looks for a fuzzy match (with difflib), but there is still a bottleneck due to an iteration over all registered envs:
https://github.com/openai/gym/blob/dcd185843a62953e27c2d54dc8c2d647d604b635/gym/envs/registration.py#L379-L403

def _check_spec_register(spec: EnvSpec):
    """Checks whether the spec is valid to be registered. Helper function for `register`."""
    global registry
    latest_versioned_spec = max(
        (
            spec_
            for spec_ in registry.values()
            if spec_.namespace == spec.namespace
            and spec_.name == spec.name
            and spec_.version is not None
        ),
        key=lambda spec_: int(spec_.version),  # type: ignore
        default=None,
    )


    unversioned_spec = next(
        (
            spec_
            for spec_ in registry.values()
            if spec_.namespace == spec.namespace
            and spec_.name == spec.name
            and spec_.version is None
        ),
        None,
    )

thus still yielding bad performance (definitely not as bad as v0.22 & v0.23 though):

$ python import_tests.py
time = 8.575s

I'm not sure what the solution for lbforaging is here?

• Advise users that gym v0.22 & v0.23 are incompatible
• Don't pre-register loads of environment configs. Perhaps do a lazy registration only when user asks for a given set-up? Not sure if/why this isn't a preferred approach.

Note: I am aware that gym has officially moved to https://github.com/Farama-Foundation/Gymnasium, but the issue remains there with gymnasium v0.27:
https://github.com/Farama-Foundation/Gymnasium/blob/6f35e7f87fc5b455b8cc70e366016c463fa52850/gymnasium/envs/registration.py#L298-L321

[callumtilbury]

PS: GitHub Markdown should renders code blocks that are linked from GitHub itself, but that isn't working here (weirdly). I've pasted the code manually.

[mateuslevisf]

+1. Basically makes the environment unusable unless you fork the repo and work from there.