Unity 机器学习代理工具包 (ML-Agents) 是一个开源项目,它使游戏和模拟能够作为训练智能代理的环境。
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# @generated by generate_proto_mypy_stubs.py. Do not edit!
import sys
from google.protobuf.descriptor import (
Descriptor as google___protobuf___descriptor___Descriptor,
)
from google.protobuf.internal.containers import (
RepeatedCompositeFieldContainer as google___protobuf___internal___containers___RepeatedCompositeFieldContainer,
RepeatedScalarFieldContainer as google___protobuf___internal___containers___RepeatedScalarFieldContainer,
)
from google.protobuf.message import (
Message as google___protobuf___message___Message,
)
from mlagents.envs.communicator_objects.resolution_pb2 import (
ResolutionProto as mlagents___envs___communicator_objects___resolution_pb2___ResolutionProto,
)
from mlagents.envs.communicator_objects.space_type_pb2 import (
SpaceTypeProto as mlagents___envs___communicator_objects___space_type_pb2___SpaceTypeProto,
)
from typing import (
Iterable as typing___Iterable,
Optional as typing___Optional,
Text as typing___Text,
)
from typing_extensions import (
Literal as typing_extensions___Literal,
)
builtin___bool = bool
builtin___bytes = bytes
builtin___float = float
builtin___int = int
class BrainParametersProto(google___protobuf___message___Message):
DESCRIPTOR: google___protobuf___descriptor___Descriptor = ...
vector_observation_size = ... # type: builtin___int
num_stacked_vector_observations = ... # type: builtin___int
vector_action_size = ... # type: google___protobuf___internal___containers___RepeatedScalarFieldContainer[builtin___int]
vector_action_descriptions = ... # type: google___protobuf___internal___containers___RepeatedScalarFieldContainer[typing___Text]
vector_action_space_type = ... # type: mlagents___envs___communicator_objects___space_type_pb2___SpaceTypeProto
brain_name = ... # type: typing___Text
is_training = ... # type: builtin___bool
@property
def camera_resolutions(self) -> google___protobuf___internal___containers___RepeatedCompositeFieldContainer[mlagents___envs___communicator_objects___resolution_pb2___ResolutionProto]: ...
def __init__(self,
*,
vector_observation_size : typing___Optional[builtin___int] = None,
num_stacked_vector_observations : typing___Optional[builtin___int] = None,
vector_action_size : typing___Optional[typing___Iterable[builtin___int]] = None,
camera_resolutions : typing___Optional[typing___Iterable[mlagents___envs___communicator_objects___resolution_pb2___ResolutionProto]] = None,
vector_action_descriptions : typing___Optional[typing___Iterable[typing___Text]] = None,
vector_action_space_type : typing___Optional[mlagents___envs___communicator_objects___space_type_pb2___SpaceTypeProto] = None,
brain_name : typing___Optional[typing___Text] = None,
is_training : typing___Optional[builtin___bool] = None,
) -> None: ...
@classmethod
def FromString(cls, s: builtin___bytes) -> BrainParametersProto: ...
def MergeFrom(self, other_msg: google___protobuf___message___Message) -> None: ...
def CopyFrom(self, other_msg: google___protobuf___message___Message) -> None: ...
if sys.version_info >= (3,):
def ClearField(self, field_name: typing_extensions___Literal[u"brain_name",u"camera_resolutions",u"is_training",u"num_stacked_vector_observations",u"vector_action_descriptions",u"vector_action_size",u"vector_action_space_type",u"vector_observation_size"]) -> None: ...
else:
def ClearField(self, field_name: typing_extensions___Literal[u"brain_name",b"brain_name",u"camera_resolutions",b"camera_resolutions",u"is_training",b"is_training",u"num_stacked_vector_observations",b"num_stacked_vector_observations",u"vector_action_descriptions",b"vector_action_descriptions",u"vector_action_size",b"vector_action_size",u"vector_action_space_type",b"vector_action_space_type",u"vector_observation_size",b"vector_observation_size"]) -> None: ...