check_env_specs

torchrl.envs.utils.check_env_specs(env, return_contiguous=True, check_dtype=True, seed: Optional[int] = None, tensordict: Optional[TensorDictBase] = None)

Tests an environment specs against the results of short rollout.

This test function should be used as a sanity check for an env wrapped with torchrl’s EnvBase subclasses: any discrepancy between the expected data and the data collected should raise an assertion error.

A broken environment spec will likely make it impossible to use parallel environments.

Parameters:

• env (EnvBase) – the env for which the specs have to be checked against data.

• return_contiguous (bool, optional) – if True, the random rollout will be called with return_contiguous=True. This will fail in some cases (e.g. heterogeneous shapes of inputs/outputs). Defaults to True.

• check_dtype (bool, optional) – if False, dtype checks will be skipped. Defaults to True.

• seed (int, optional) – for reproducibility, a seed can be set. The seed will be set in pytorch temporarily, then the RNG state will be reverted to what it was before. For the env, we set the seed but since setting the rng state back to what is was isn’t a feature of most environment, we leave it to the user to accomplish that. Defaults to None.

• tensordict (TensorDict, optional) – an optional tensordict instance to use for reset.

Caution: this function resets the env seed. It should be used “offline” to check that an env is adequately constructed, but it may affect the seeding of an experiment and as such should be kept out of training scripts.