def __init__(self, venv: vec_env.VecEnv, expert_data: Union[(Iterable[Mapping], types.Transitions)], expert_batch_size: int, gen_algo: on_policy_algorithm.OnPolicyAlgorithm, discrim=None, *, discrim_kwargs: Optional[Mapping]=None, **kwargs):
    'Generative Adversarial Imitation Learning that accepts Image Obs\n\n        Most parameters are described in and passed to `AdversarialTrainer.__init__`.\n        Additional parameters that `CNNGAIL` adds on top of its superclass initializer are\n        as follows:\n\n        Args:\n            discrim_kwargs: Optional keyword arguments to use while constructing the\n                DiscrimNetGAIL.\n\n        '
    discrim_kwargs = (discrim_kwargs or {})
    if (discrim == None):
        discrim = discrim_nets.DiscrimNetGAIL(venv.observation_space, venv.action_space, discrim_net=ActObsCNN, **discrim_kwargs)
    logging.info('using CNN GAIL')
    super().__init__(venv, gen_algo, discrim, expert_data, expert_batch_size, **kwargs)
Generative Adversarial Imitation Learning that accepts Image Obs

Most parameters are described in and passed to `AdversarialTrainer.__init__`.
Additional parameters that `CNNGAIL` adds on top of its superclass initializer are
as follows:

Args:
    discrim_kwargs: Optional keyword arguments to use while constructing the
        DiscrimNetGAIL.
def __init__(self, venv: vec_env.VecEnv, expert_data: Union[(Iterable[Mapping], types.Transitions)], expert_batch_size: int, gen_algo: on_policy_algorithm.OnPolicyAlgorithm, discrim=None, *, discrim_kwargs: Optional[Mapping]=None, **kwargs):
    'Generative Adversarial Imitation Learning that accepts Image Obs\n\n        Most parameters are described in and passed to `AdversarialTrainer.__init__`.\n        Additional parameters that `CNNGAIL` adds on top of its superclass initializer are\n        as follows:\n\n        Args:\n            discrim_kwargs: Optional keyword arguments to use while constructing the\n                DiscrimNetGAIL.\n\n        '
    discrim_kwargs = (discrim_kwargs or {})
    if (discrim == None):
        discrim = discrim_nets.DiscrimNetGAIL(venv.observation_space, venv.action_space, discrim_net=ActObsCNN, **discrim_kwargs)
    logging.info('using CNN GAIL')
    super().__init__(venv, gen_algo, discrim, expert_data, expert_batch_size, **kwargs)