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/agent_info_action_pair.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 agent_info_pb2 as mlagents__envs_dot_communicator__objects_dot_agent__info__pb2
from mlagents_envs.communicator_objects import agent_action_pb2 as mlagents__envs_dot_communicator__objects_dot_agent__action__pb2
DESCRIPTOR = _descriptor.FileDescriptor(
name='mlagents_envs/communicator_objects/agent_info_action_pair.proto',
package='communicator_objects',
syntax='proto3',
serialized_pb=_b('\n?mlagents_envs/communicator_objects/agent_info_action_pair.proto\x12\x14\x63ommunicator_objects\x1a\x33mlagents_envs/communicator_objects/agent_info.proto\x1a\x35mlagents_envs/communicator_objects/agent_action.proto\"\x91\x01\n\x18\x41gentInfoActionPairProto\x12\x38\n\nagent_info\x18\x01 \x01(\x0b\x32$.communicator_objects.AgentInfoProto\x12;\n\x0b\x61\x63tion_info\x18\x02 \x01(\x0b\x32&.communicator_objects.AgentActionProtoB%\xaa\x02\"Unity.MLAgents.CommunicatorObjectsb\x06proto3')
,
dependencies=[mlagents__envs_dot_communicator__objects_dot_agent__info__pb2.DESCRIPTOR,mlagents__envs_dot_communicator__objects_dot_agent__action__pb2.DESCRIPTOR,])
_AGENTINFOACTIONPAIRPROTO = _descriptor.Descriptor(
name='AgentInfoActionPairProto',
full_name='communicator_objects.AgentInfoActionPairProto',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
_descriptor.FieldDescriptor(
name='agent_info', full_name='communicator_objects.AgentInfoActionPairProto.agent_info', 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='action_info', full_name='communicator_objects.AgentInfoActionPairProto.action_info', 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=198,
serialized_end=343,
)
_AGENTINFOACTIONPAIRPROTO.fields_by_name['agent_info'].message_type = mlagents__envs_dot_communicator__objects_dot_agent__info__pb2._AGENTINFOPROTO
_AGENTINFOACTIONPAIRPROTO.fields_by_name['action_info'].message_type = mlagents__envs_dot_communicator__objects_dot_agent__action__pb2._AGENTACTIONPROTO
DESCRIPTOR.message_types_by_name['AgentInfoActionPairProto'] = _AGENTINFOACTIONPAIRPROTO
_sym_db.RegisterFileDescriptor(DESCRIPTOR)
AgentInfoActionPairProto = _reflection.GeneratedProtocolMessageType('AgentInfoActionPairProto', (_message.Message,), dict(
DESCRIPTOR = _AGENTINFOACTIONPAIRPROTO,
__module__ = 'mlagents_envs.communicator_objects.agent_info_action_pair_pb2'
# @@protoc_insertion_point(class_scope:communicator_objects.AgentInfoActionPairProto)
))
_sym_db.RegisterMessage(AgentInfoActionPairProto)
DESCRIPTOR.has_options = True
DESCRIPTOR._options = _descriptor._ParseOptions(descriptor_pb2.FileOptions(), _b('\252\002\"Unity.MLAgents.CommunicatorObjects'))
# @@protoc_insertion_point(module_scope)