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 the protocol buffer compiler. DO NOT EDIT!
# source: mlagents_envs/communicator_objects/unity_output.proto
import sys
_b=sys.version_info[0]<3 and (lambda x:x) or (lambda x:x.encode('latin1'))
from google.protobuf import descriptor as _descriptor
from google.protobuf import message as _message
from google.protobuf import reflection as _reflection
from google.protobuf import symbol_database as _symbol_database
from google.protobuf import descriptor_pb2
# @@protoc_insertion_point(imports)
_sym_db = _symbol_database.Default()
from mlagents_envs.communicator_objects import unity_rl_output_pb2 as mlagents__envs_dot_communicator__objects_dot_unity__rl__output__pb2
from mlagents_envs.communicator_objects import unity_rl_initialization_output_pb2 as mlagents__envs_dot_communicator__objects_dot_unity__rl__initialization__output__pb2
DESCRIPTOR = _descriptor.FileDescriptor(
name='mlagents_envs/communicator_objects/unity_output.proto',
package='communicator_objects',
syntax='proto3',
serialized_pb=_b('\n5mlagents_envs/communicator_objects/unity_output.proto\x12\x14\x63ommunicator_objects\x1a\x38mlagents_envs/communicator_objects/unity_rl_output.proto\x1aGmlagents_envs/communicator_objects/unity_rl_initialization_output.proto\"\xa9\x01\n\x10UnityOutputProto\x12;\n\trl_output\x18\x01 \x01(\x0b\x32(.communicator_objects.UnityRLOutputProto\x12X\n\x18rl_initialization_output\x18\x02 \x01(\x0b\x32\x36.communicator_objects.UnityRLInitializationOutputProtoB\x1f\xaa\x02\x1cMLAgents.CommunicatorObjectsb\x06proto3')
,
dependencies=[mlagents__envs_dot_communicator__objects_dot_unity__rl__output__pb2.DESCRIPTOR,mlagents__envs_dot_communicator__objects_dot_unity__rl__initialization__output__pb2.DESCRIPTOR,])
_UNITYOUTPUTPROTO = _descriptor.Descriptor(
name='UnityOutputProto',
full_name='communicator_objects.UnityOutputProto',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
_descriptor.FieldDescriptor(
name='rl_output', full_name='communicator_objects.UnityOutputProto.rl_output', index=0,
number=1, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='rl_initialization_output', full_name='communicator_objects.UnityOutputProto.rl_initialization_output', index=1,
number=2, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None, file=DESCRIPTOR),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
syntax='proto3',
extension_ranges=[],
oneofs=[
],
serialized_start=211,
serialized_end=380,
)
_UNITYOUTPUTPROTO.fields_by_name['rl_output'].message_type = mlagents__envs_dot_communicator__objects_dot_unity__rl__output__pb2._UNITYRLOUTPUTPROTO
_UNITYOUTPUTPROTO.fields_by_name['rl_initialization_output'].message_type = mlagents__envs_dot_communicator__objects_dot_unity__rl__initialization__output__pb2._UNITYRLINITIALIZATIONOUTPUTPROTO
DESCRIPTOR.message_types_by_name['UnityOutputProto'] = _UNITYOUTPUTPROTO
_sym_db.RegisterFileDescriptor(DESCRIPTOR)
UnityOutputProto = _reflection.GeneratedProtocolMessageType('UnityOutputProto', (_message.Message,), dict(
DESCRIPTOR = _UNITYOUTPUTPROTO,
__module__ = 'mlagents_envs.communicator_objects.unity_output_pb2'
# @@protoc_insertion_point(class_scope:communicator_objects.UnityOutputProto)
))
_sym_db.RegisterMessage(UnityOutputProto)
DESCRIPTOR.has_options = True
DESCRIPTOR._options = _descriptor._ParseOptions(descriptor_pb2.FileOptions(), _b('\252\002\034MLAgents.CommunicatorObjects'))
# @@protoc_insertion_point(module_scope)