Closed Radagaisus closed 2 years ago
I am just beginning to use the library but AFAIK, in the meantime, you can disable_env_checker=True
in gym.make()
That's a good point, we had some discussions about some sort of a global disable option of the env checker, but it fell through the cracks since we solved its main issue in a different way.
It might make sense to just make disable_env_checker
to be a part of the environment spec. Then you can manually disable it as an env developer, and it can be reenabled by end-users in make
.
BTW an easy way to kinda do this is by setting gym.logger.set_level(ERROR)
somewhere in your code, it should silence all the warnings. It's not the exact same thing, but might be close enough
@Radagaisus FYI, the environment checker is going to have a major bug fix the next version (v25) as it currently has a number of significant issues. This is the new PR, https://github.com/openai/gym/pull/2903
@RedTachyon I like the idea, would we want to change disable_env_checker: Optional[bool] = None
to detect if we should overwrite the register spec.
Closing in favor of #2903
Proposal
A way for environment developers to suppress warnings, either globally or on a per-type basis.
Motivation
I’m developing a simple environment that receives some well-intentioned but totally wrong for my use case warnings (“WARN: Agent's maximum action space value is infinity”, “We recommend you to use a symmetric and normalized Box action space”). Nothing for me to do to resolve them, and I don’t want them to pop up, confuse, and sow doubt for the end-user.
Pitch
I think the natural place to manage this config is during registration:
If an end-user is trying to debug an issue with the environment, they might force it to be re-enabled by setting a global
gym.warnings=True
flag.Checklist