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_input.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_input_pb2 as mlagents__envs_dot_communicator__objects_dot_unity__rl__input__pb2
from mlagents_envs.communicator_objects import unity_rl_initialization_input_pb2 as mlagents__envs_dot_communicator__objects_dot_unity__rl__initialization__input__pb2
DESCRIPTOR = _descriptor.FileDescriptor(
name='mlagents_envs/communicator_objects/unity_input.proto',
package='communicator_objects',
syntax='proto3',
serialized_pb=_b('\n4mlagents_envs/communicator_objects/unity_input.proto\x12\x14\x63ommunicator_objects\x1a\x37mlagents_envs/communicator_objects/unity_rl_input.proto\x1a\x46mlagents_envs/communicator_objects/unity_rl_initialization_input.proto\"\xa4\x01\n\x0fUnityInputProto\x12\x39\n\x08rl_input\x18\x01 \x01(\x0b\x32\'.communicator_objects.UnityRLInputProto\x12V\n\x17rl_initialization_input\x18\x02 \x01(\x0b\x32\x35.communicator_objects.UnityRLInitializationInputProtoB\x1f\xaa\x02\x1cMLAgents.CommunicatorObjectsb\x06proto3')
,
dependencies=[mlagents__envs_dot_communicator__objects_dot_unity__rl__input__pb2.DESCRIPTOR,mlagents__envs_dot_communicator__objects_dot_unity__rl__initialization__input__pb2.DESCRIPTOR,])
_UNITYINPUTPROTO = _descriptor.Descriptor(
name='UnityInputProto',
full_name='communicator_objects.UnityInputProto',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
_descriptor.FieldDescriptor(
name='rl_input', full_name='communicator_objects.UnityInputProto.rl_input', 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_input', full_name='communicator_objects.UnityInputProto.rl_initialization_input', 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=208,
serialized_end=372,
)
_UNITYINPUTPROTO.fields_by_name['rl_input'].message_type = mlagents__envs_dot_communicator__objects_dot_unity__rl__input__pb2._UNITYRLINPUTPROTO
_UNITYINPUTPROTO.fields_by_name['rl_initialization_input'].message_type = mlagents__envs_dot_communicator__objects_dot_unity__rl__initialization__input__pb2._UNITYRLINITIALIZATIONINPUTPROTO
DESCRIPTOR.message_types_by_name['UnityInputProto'] = _UNITYINPUTPROTO
_sym_db.RegisterFileDescriptor(DESCRIPTOR)
UnityInputProto = _reflection.GeneratedProtocolMessageType('UnityInputProto', (_message.Message,), dict(
DESCRIPTOR = _UNITYINPUTPROTO,
__module__ = 'mlagents_envs.communicator_objects.unity_input_pb2'
# @@protoc_insertion_point(class_scope:communicator_objects.UnityInputProto)
))
_sym_db.RegisterMessage(UnityInputProto)
DESCRIPTOR.has_options = True
DESCRIPTOR._options = _descriptor._ParseOptions(descriptor_pb2.FileOptions(), _b('\252\002\034MLAgents.CommunicatorObjects'))
# @@protoc_insertion_point(module_scope)