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 (
RepeatedScalarFieldContainer as google___protobuf___internal___containers___RepeatedScalarFieldContainer,
)
from google.protobuf.message import (
Message as google___protobuf___message___Message,
)
from mlagents.envs.communicator_objects.custom_observation_pb2 import (
CustomObservationProto as mlagents___envs___communicator_objects___custom_observation_pb2___CustomObservationProto,
)
from typing import (
Iterable as typing___Iterable,
Optional as typing___Optional,
Text as typing___Text,
)
from typing_extensions import (
Literal as typing_extensions___Literal,
)
class AgentInfoProto(google___protobuf___message___Message):
DESCRIPTOR: google___protobuf___descriptor___Descriptor = ...
stacked_vector_observation = ... # type: google___protobuf___internal___containers___RepeatedScalarFieldContainer[float]
visual_observations = ... # type: google___protobuf___internal___containers___RepeatedScalarFieldContainer[bytes]
text_observation = ... # type: typing___Text
stored_vector_actions = ... # type: google___protobuf___internal___containers___RepeatedScalarFieldContainer[float]
stored_text_actions = ... # type: typing___Text
memories = ... # type: google___protobuf___internal___containers___RepeatedScalarFieldContainer[float]
reward = ... # type: float
done = ... # type: bool
max_step_reached = ... # type: bool
id = ... # type: int
action_mask = ... # type: google___protobuf___internal___containers___RepeatedScalarFieldContainer[bool]
@property
def custom_observation(self) -> mlagents___envs___communicator_objects___custom_observation_pb2___CustomObservationProto: ...
def __init__(self,
*,
stacked_vector_observation : typing___Optional[typing___Iterable[float]] = None,
visual_observations : typing___Optional[typing___Iterable[bytes]] = None,
text_observation : typing___Optional[typing___Text] = None,
stored_vector_actions : typing___Optional[typing___Iterable[float]] = None,
stored_text_actions : typing___Optional[typing___Text] = None,
memories : typing___Optional[typing___Iterable[float]] = None,
reward : typing___Optional[float] = None,
done : typing___Optional[bool] = None,
max_step_reached : typing___Optional[bool] = None,
id : typing___Optional[int] = None,
action_mask : typing___Optional[typing___Iterable[bool]] = None,
custom_observation : typing___Optional[mlagents___envs___communicator_objects___custom_observation_pb2___CustomObservationProto] = None,
) -> None: ...
@classmethod
def FromString(cls, s: bytes) -> AgentInfoProto: ...
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 HasField(self, field_name: typing_extensions___Literal[u"custom_observation"]) -> bool: ...
def ClearField(self, field_name: typing_extensions___Literal[u"action_mask",u"custom_observation",u"done",u"id",u"max_step_reached",u"memories",u"reward",u"stacked_vector_observation",u"stored_text_actions",u"stored_vector_actions",u"text_observation",u"visual_observations"]) -> None: ...
else:
def HasField(self, field_name: typing_extensions___Literal[u"custom_observation",b"custom_observation"]) -> bool: ...
def ClearField(self, field_name: typing_extensions___Literal[u"action_mask",b"action_mask",u"custom_observation",b"custom_observation",u"done",b"done",u"id",b"id",u"max_step_reached",b"max_step_reached",u"memories",b"memories",u"reward",b"reward",u"stacked_vector_observation",b"stacked_vector_observation",u"stored_text_actions",b"stored_text_actions",u"stored_vector_actions",b"stored_vector_actions",u"text_observation",b"text_observation",u"visual_observations",b"visual_observations"]) -> None: ...