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/custom_action.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()
DESCRIPTOR = _descriptor.FileDescriptor(
name='mlagents/envs/communicator_objects/custom_action.proto',
package='communicator_objects',
syntax='proto3',
serialized_pb=_b('\n6mlagents/envs/communicator_objects/custom_action.proto\x12\x14\x63ommunicator_objects\"\x13\n\x11\x43ustomActionProtoB\x1f\xaa\x02\x1cMLAgents.CommunicatorObjectsb\x06proto3')
)
_CUSTOMACTIONPROTO = _descriptor.Descriptor(
name='CustomActionProto',
full_name='communicator_objects.CustomActionProto',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
syntax='proto3',
extension_ranges=[],
oneofs=[
],
serialized_start=80,
serialized_end=99,
)
DESCRIPTOR.message_types_by_name['CustomActionProto'] = _CUSTOMACTIONPROTO
_sym_db.RegisterFileDescriptor(DESCRIPTOR)
CustomActionProto = _reflection.GeneratedProtocolMessageType('CustomActionProto', (_message.Message,), dict(
DESCRIPTOR = _CUSTOMACTIONPROTO,
__module__ = 'mlagents.envs.communicator_objects.custom_action_pb2'
# @@protoc_insertion_point(class_scope:communicator_objects.CustomActionProto)
))
_sym_db.RegisterMessage(CustomActionProto)
DESCRIPTOR.has_options = True
DESCRIPTOR._options = _descriptor._ParseOptions(descriptor_pb2.FileOptions(), _b('\252\002\034MLAgents.CommunicatorObjects'))
# @@protoc_insertion_point(module_scope)