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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558 行
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import atexit
import glob
import uuid
import numpy as np
import os
import subprocess
from typing import Dict, List, Optional, Any, Tuple
import mlagents_envs
from mlagents_envs.logging_util import get_logger
from mlagents_envs.side_channel.side_channel import SideChannel, IncomingMessage
from mlagents_envs.base_env import (
BaseEnv,
DecisionSteps,
TerminalSteps,
BehaviorSpec,
BehaviorName,
AgentId,
)
from mlagents_envs.timers import timed, hierarchical_timer
from mlagents_envs.exception import (
UnityEnvironmentException,
UnityCommunicationException,
UnityActionException,
UnityTimeOutException,
)
from mlagents_envs.communicator_objects.command_pb2 import STEP, RESET
from mlagents_envs.rpc_utils import behavior_spec_from_proto, steps_from_proto
from mlagents_envs.communicator_objects.unity_rl_input_pb2 import UnityRLInputProto
from mlagents_envs.communicator_objects.unity_rl_output_pb2 import UnityRLOutputProto
from mlagents_envs.communicator_objects.agent_action_pb2 import AgentActionProto
from mlagents_envs.communicator_objects.unity_output_pb2 import UnityOutputProto
from mlagents_envs.communicator_objects.unity_rl_initialization_input_pb2 import (
UnityRLInitializationInputProto,
)
from mlagents_envs.communicator_objects.unity_input_pb2 import UnityInputProto
from .rpc_communicator import RpcCommunicator
from sys import platform
import signal
import struct
logger = get_logger(__name__)
class UnityEnvironment(BaseEnv):
SCALAR_ACTION_TYPES = (int, np.int32, np.int64, float, np.float32, np.float64)
SINGLE_BRAIN_ACTION_TYPES = SCALAR_ACTION_TYPES + (list, np.ndarray)
# Communication protocol version.
# When connecting to C#, this must match Academy.k_ApiVersion
# Currently we require strict equality between the communication protocol
# on each side, although we may allow some flexibility in the future.
# This should be incremented whenever a change is made to the communication protocol.
API_VERSION = "0.16.0"
# Default port that the editor listens on. If an environment executable
# isn't specified, this port will be used.
DEFAULT_EDITOR_PORT = 5004
# Default base port for environments. Each environment will be offset from this
# by it's worker_id.
BASE_ENVIRONMENT_PORT = 5005
# Command line argument used to pass the port to the executable environment.
PORT_COMMAND_LINE_ARG = "--mlagents-port"
def __init__(
self,
file_name: Optional[str] = None,
worker_id: int = 0,
base_port: Optional[int] = None,
seed: int = 0,
no_graphics: bool = False,
timeout_wait: int = 60,
args: Optional[List[str]] = None,
side_channels: Optional[List[SideChannel]] = None,
):
"""
Starts a new unity environment and establishes a connection with the environment.
Notice: Currently communication between Unity and Python takes place over an open socket without authentication.
Ensure that the network where training takes place is secure.
:string file_name: Name of Unity environment binary.
:int base_port: Baseline port number to connect to Unity environment over. worker_id increments over this.
If no environment is specified (i.e. file_name is None), the DEFAULT_EDITOR_PORT will be used.
:int worker_id: Offset from base_port. Used for training multiple environments simultaneously.
:bool no_graphics: Whether to run the Unity simulator in no-graphics mode
:int timeout_wait: Time (in seconds) to wait for connection from environment.
:list args: Addition Unity command line arguments
:list side_channels: Additional side channel for no-rl communication with Unity
"""
args = args or []
atexit.register(self._close)
# If base port is not specified, use BASE_ENVIRONMENT_PORT if we have
# an environment, otherwise DEFAULT_EDITOR_PORT
if base_port is None:
base_port = (
self.BASE_ENVIRONMENT_PORT if file_name else self.DEFAULT_EDITOR_PORT
)
self.port = base_port + worker_id
self._buffer_size = 12000
# If true, this means the environment was successfully loaded
self._loaded = False
# The process that is started. If None, no process was started
self.proc1 = None
self.timeout_wait: int = timeout_wait
self.communicator = self.get_communicator(worker_id, base_port, timeout_wait)
self.worker_id = worker_id
self.side_channels: Dict[uuid.UUID, SideChannel] = {}
if side_channels is not None:
for _sc in side_channels:
if _sc.channel_id in self.side_channels:
raise UnityEnvironmentException(
"There cannot be two side channels with the same channel id {0}.".format(
_sc.channel_id
)
)
self.side_channels[_sc.channel_id] = _sc
# If the environment name is None, a new environment will not be launched
# and the communicator will directly try to connect to an existing unity environment.
# If the worker-id is not 0 and the environment name is None, an error is thrown
if file_name is None and worker_id != 0:
raise UnityEnvironmentException(
"If the environment name is None, "
"the worker-id must be 0 in order to connect with the Editor."
)
if file_name is not None:
self.executable_launcher(file_name, no_graphics, args)
else:
logger.info(
f"Listening on port {self.port}. "
f"Start training by pressing the Play button in the Unity Editor."
)
self._loaded = True
rl_init_parameters_in = UnityRLInitializationInputProto(
seed=seed,
communication_version=self.API_VERSION,
package_version=mlagents_envs.__version__,
)
try:
aca_output = self.send_academy_parameters(rl_init_parameters_in)
aca_params = aca_output.rl_initialization_output
except UnityTimeOutException:
self._close(0)
raise
unity_communicator_version = aca_params.communication_version
if unity_communicator_version != UnityEnvironment.API_VERSION:
self._close(0)
raise UnityEnvironmentException(
f"The communication API version is not compatible between Unity and python. "
f"Python API: {UnityEnvironment.API_VERSION}, Unity API: {unity_communicator_version}.\n "
f"Please go to https://github.com/Unity-Technologies/ml-agents/releases/tag/latest_release "
f"to download the latest version of ML-Agents."
)
else:
logger.info(
f"Connected to Unity environment with package version {aca_params.package_version} "
f"and communication version {aca_params.communication_version}"
)
self._env_state: Dict[str, Tuple[DecisionSteps, TerminalSteps]] = {}
self._env_specs: Dict[str, BehaviorSpec] = {}
self._env_actions: Dict[str, np.ndarray] = {}
self._is_first_message = True
self._update_behavior_specs(aca_output)
@staticmethod
def get_communicator(worker_id, base_port, timeout_wait):
return RpcCommunicator(worker_id, base_port, timeout_wait)
@staticmethod
def validate_environment_path(env_path: str) -> Optional[str]:
# Strip out executable extensions if passed
env_path = (
env_path.strip()
.replace(".app", "")
.replace(".exe", "")
.replace(".x86_64", "")
.replace(".x86", "")
)
true_filename = os.path.basename(os.path.normpath(env_path))
logger.debug("The true file name is {}".format(true_filename))
if not (glob.glob(env_path) or glob.glob(env_path + ".*")):
return None
cwd = os.getcwd()
launch_string = None
true_filename = os.path.basename(os.path.normpath(env_path))
if platform == "linux" or platform == "linux2":
candidates = glob.glob(os.path.join(cwd, env_path) + ".x86_64")
if len(candidates) == 0:
candidates = glob.glob(os.path.join(cwd, env_path) + ".x86")
if len(candidates) == 0:
candidates = glob.glob(env_path + ".x86_64")
if len(candidates) == 0:
candidates = glob.glob(env_path + ".x86")
if len(candidates) > 0:
launch_string = candidates[0]
elif platform == "darwin":
candidates = glob.glob(
os.path.join(cwd, env_path + ".app", "Contents", "MacOS", true_filename)
)
if len(candidates) == 0:
candidates = glob.glob(
os.path.join(env_path + ".app", "Contents", "MacOS", true_filename)
)
if len(candidates) == 0:
candidates = glob.glob(
os.path.join(cwd, env_path + ".app", "Contents", "MacOS", "*")
)
if len(candidates) == 0:
candidates = glob.glob(
os.path.join(env_path + ".app", "Contents", "MacOS", "*")
)
if len(candidates) > 0:
launch_string = candidates[0]
elif platform == "win32":
candidates = glob.glob(os.path.join(cwd, env_path + ".exe"))
if len(candidates) == 0:
candidates = glob.glob(env_path + ".exe")
if len(candidates) > 0:
launch_string = candidates[0]
return launch_string
def executable_launcher(self, file_name, no_graphics, args):
launch_string = self.validate_environment_path(file_name)
if launch_string is None:
self._close(0)
raise UnityEnvironmentException(
f"Couldn't launch the {file_name} environment. Provided filename does not match any environments."
)
else:
logger.debug("This is the launch string {}".format(launch_string))
# Launch Unity environment
subprocess_args = [launch_string]
if no_graphics:
subprocess_args += ["-nographics", "-batchmode"]
subprocess_args += [UnityEnvironment.PORT_COMMAND_LINE_ARG, str(self.port)]
subprocess_args += args
try:
self.proc1 = subprocess.Popen(
subprocess_args,
# start_new_session=True means that signals to the parent python process
# (e.g. SIGINT from keyboard interrupt) will not be sent to the new process on POSIX platforms.
# This is generally good since we want the environment to have a chance to shutdown,
# but may be undesirable in come cases; if so, we'll add a command-line toggle.
# Note that on Windows, the CTRL_C signal will still be sent.
start_new_session=True,
)
except PermissionError as perm:
# This is likely due to missing read or execute permissions on file.
raise UnityEnvironmentException(
f"Error when trying to launch environment - make sure "
f"permissions are set correctly. For example "
f'"chmod -R 755 {launch_string}"'
) from perm
def _update_behavior_specs(self, output: UnityOutputProto) -> None:
init_output = output.rl_initialization_output
for brain_param in init_output.brain_parameters:
# Each BrainParameter in the rl_initialization_output should have at least one AgentInfo
# Get that agent, because we need some of its observations.
agent_infos = output.rl_output.agentInfos[brain_param.brain_name]
if agent_infos.value:
agent = agent_infos.value[0]
new_spec = behavior_spec_from_proto(brain_param, agent)
self._env_specs[brain_param.brain_name] = new_spec
logger.info(f"Connected new brain:\n{brain_param.brain_name}")
def _update_state(self, output: UnityRLOutputProto) -> None:
"""
Collects experience information from all external brains in environment at current step.
"""
for brain_name in self._env_specs.keys():
if brain_name in output.agentInfos:
agent_info_list = output.agentInfos[brain_name].value
self._env_state[brain_name] = steps_from_proto(
agent_info_list, self._env_specs[brain_name]
)
else:
self._env_state[brain_name] = (
DecisionSteps.empty(self._env_specs[brain_name]),
TerminalSteps.empty(self._env_specs[brain_name]),
)
self._parse_side_channel_message(self.side_channels, output.side_channel)
def reset(self) -> None:
if self._loaded:
outputs = self.communicator.exchange(self._generate_reset_input())
if outputs is None:
raise UnityCommunicationException("Communicator has stopped.")
self._update_behavior_specs(outputs)
rl_output = outputs.rl_output
self._update_state(rl_output)
self._is_first_message = False
self._env_actions.clear()
else:
raise UnityEnvironmentException("No Unity environment is loaded.")
@timed
def step(self) -> None:
if self._is_first_message:
return self.reset()
if not self._loaded:
raise UnityEnvironmentException("No Unity environment is loaded.")
# fill the blanks for missing actions
for group_name in self._env_specs:
if group_name not in self._env_actions:
n_agents = 0
if group_name in self._env_state:
n_agents = len(self._env_state[group_name][0])
self._env_actions[group_name] = self._env_specs[
group_name
].create_empty_action(n_agents)
step_input = self._generate_step_input(self._env_actions)
with hierarchical_timer("communicator.exchange"):
outputs = self.communicator.exchange(step_input)
if outputs is None:
raise UnityCommunicationException("Communicator has stopped.")
self._update_behavior_specs(outputs)
rl_output = outputs.rl_output
self._update_state(rl_output)
self._env_actions.clear()
def get_behavior_names(self):
return list(self._env_specs.keys())
def _assert_behavior_exists(self, behavior_name: str) -> None:
if behavior_name not in self._env_specs:
raise UnityActionException(
"The group {0} does not correspond to an existing agent group "
"in the environment".format(behavior_name)
)
def set_actions(self, behavior_name: BehaviorName, action: np.ndarray) -> None:
self._assert_behavior_exists(behavior_name)
if behavior_name not in self._env_state:
return
spec = self._env_specs[behavior_name]
expected_type = np.float32 if spec.is_action_continuous() else np.int32
expected_shape = (len(self._env_state[behavior_name][0]), spec.action_size)
if action.shape != expected_shape:
raise UnityActionException(
"The behavior {0} needs an input of dimension {1} but received input of dimension {2}".format(
behavior_name, expected_shape, action.shape
)
)
if action.dtype != expected_type:
action = action.astype(expected_type)
self._env_actions[behavior_name] = action
def set_action_for_agent(
self, behavior_name: BehaviorName, agent_id: AgentId, action: np.ndarray
) -> None:
self._assert_behavior_exists(behavior_name)
if behavior_name not in self._env_state:
return
spec = self._env_specs[behavior_name]
expected_shape = (spec.action_size,)
if action.shape != expected_shape:
raise UnityActionException(
f"The Agent {0} with BehaviorName {1} needs an input of dimension "
f"{2} but received input of dimension {3}".format(
agent_id, behavior_name, expected_shape, action.shape
)
)
expected_type = np.float32 if spec.is_action_continuous() else np.int32
if action.dtype != expected_type:
action = action.astype(expected_type)
if behavior_name not in self._env_actions:
self._env_actions[behavior_name] = spec.create_empty_action(
len(self._env_state[behavior_name][0])
)
try:
index = np.where(self._env_state[behavior_name][0].agent_id == agent_id)[0][
0
]
except IndexError as ie:
raise IndexError(
"agent_id {} is did not request a decision at the previous step".format(
agent_id
)
) from ie
self._env_actions[behavior_name][index] = action
def get_steps(
self, behavior_name: BehaviorName
) -> Tuple[DecisionSteps, TerminalSteps]:
self._assert_behavior_exists(behavior_name)
return self._env_state[behavior_name]
def get_behavior_spec(self, behavior_name: BehaviorName) -> BehaviorSpec:
self._assert_behavior_exists(behavior_name)
return self._env_specs[behavior_name]
def close(self):
"""
Sends a shutdown signal to the unity environment, and closes the socket connection.
"""
if self._loaded:
self._close()
else:
raise UnityEnvironmentException("No Unity environment is loaded.")
def _close(self, timeout: Optional[int] = None) -> None:
"""
Close the communicator and environment subprocess (if necessary).
:int timeout: [Optional] Number of seconds to wait for the environment to shut down before
force-killing it. Defaults to `self.timeout_wait`.
"""
if timeout is None:
timeout = self.timeout_wait
self._loaded = False
self.communicator.close()
if self.proc1 is not None:
# Wait a bit for the process to shutdown, but kill it if it takes too long
try:
self.proc1.wait(timeout=timeout)
signal_name = self.returncode_to_signal_name(self.proc1.returncode)
signal_name = f" ({signal_name})" if signal_name else ""
return_info = f"Environment shut down with return code {self.proc1.returncode}{signal_name}."
logger.info(return_info)
except subprocess.TimeoutExpired:
logger.info("Environment timed out shutting down. Killing...")
self.proc1.kill()
# Set to None so we don't try to close multiple times.
self.proc1 = None
@classmethod
def _flatten(cls, arr: Any) -> List[float]:
"""
Converts arrays to list.
:param arr: numpy vector.
:return: flattened list.
"""
if isinstance(arr, cls.SCALAR_ACTION_TYPES):
arr = [float(arr)]
if isinstance(arr, np.ndarray):
arr = arr.tolist()
if len(arr) == 0:
return arr
if isinstance(arr[0], np.ndarray):
# pylint: disable=no-member
arr = [item for sublist in arr for item in sublist.tolist()]
if isinstance(arr[0], list):
# pylint: disable=not-an-iterable
arr = [item for sublist in arr for item in sublist]
arr = [float(x) for x in arr]
return arr
@staticmethod
def _parse_side_channel_message(
side_channels: Dict[uuid.UUID, SideChannel], data: bytes
) -> None:
offset = 0
while offset < len(data):
try:
channel_id = uuid.UUID(bytes_le=bytes(data[offset : offset + 16]))
offset += 16
message_len, = struct.unpack_from("<i", data, offset)
offset = offset + 4
message_data = data[offset : offset + message_len]
offset = offset + message_len
except Exception:
raise UnityEnvironmentException(
"There was a problem reading a message in a SideChannel. "
"Please make sure the version of MLAgents in Unity is "
"compatible with the Python version."
)
if len(message_data) != message_len:
raise UnityEnvironmentException(
"The message received by the side channel {0} was "
"unexpectedly short. Make sure your Unity Environment "
"sending side channel data properly.".format(channel_id)
)
if channel_id in side_channels:
incoming_message = IncomingMessage(message_data)
side_channels[channel_id].on_message_received(incoming_message)
else:
logger.warning(
"Unknown side channel data received. Channel type "
": {0}.".format(channel_id)
)
@staticmethod
def _generate_side_channel_data(
side_channels: Dict[uuid.UUID, SideChannel]
) -> bytearray:
result = bytearray()
for channel_id, channel in side_channels.items():
for message in channel.message_queue:
result += channel_id.bytes_le
result += struct.pack("<i", len(message))
result += message
channel.message_queue = []
return result
@timed
def _generate_step_input(
self, vector_action: Dict[str, np.ndarray]
) -> UnityInputProto:
rl_in = UnityRLInputProto()
for b in vector_action:
n_agents = len(self._env_state[b][0])
if n_agents == 0:
continue
for i in range(n_agents):
action = AgentActionProto(vector_actions=vector_action[b][i])
rl_in.agent_actions[b].value.extend([action])
rl_in.command = STEP
rl_in.side_channel = bytes(self._generate_side_channel_data(self.side_channels))
return self.wrap_unity_input(rl_in)
def _generate_reset_input(self) -> UnityInputProto:
rl_in = UnityRLInputProto()
rl_in.command = RESET
rl_in.side_channel = bytes(self._generate_side_channel_data(self.side_channels))
return self.wrap_unity_input(rl_in)
def send_academy_parameters(
self, init_parameters: UnityRLInitializationInputProto
) -> UnityOutputProto:
inputs = UnityInputProto()
inputs.rl_initialization_input.CopyFrom(init_parameters)
return self.communicator.initialize(inputs)
@staticmethod
def wrap_unity_input(rl_input: UnityRLInputProto) -> UnityInputProto:
result = UnityInputProto()
result.rl_input.CopyFrom(rl_input)
return result
@staticmethod
def returncode_to_signal_name(returncode: int) -> Optional[str]:
"""
Try to convert return codes into their corresponding signal name.
E.g. returncode_to_signal_name(-2) -> "SIGINT"
"""
try:
# A negative value -N indicates that the child was terminated by signal N (POSIX only).
s = signal.Signals(-returncode) # pylint: disable=no-member
return s.name
except Exception:
# Should generally be a ValueError, but catch everything just in case.
return None