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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491 行
23 KiB

import atexit
import io
import glob
import json
import logging
import numpy as np
import os
import socket
import subprocess
import struct
from .brain import BrainInfo, BrainParameters, AllBrainInfo
from .exception import UnityEnvironmentException, UnityActionException, UnityTimeOutException
from .curriculum import Curriculum
from PIL import Image
from sys import platform
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger("unityagents")
class UnityEnvironment(object):
def __init__(self, file_name, worker_id=0,
base_port=5005, curriculum=None,
seed=0):
"""
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.
:int worker_id: Number to add to communication port (5005) [0]. Used for asynchronous agent scenarios.
"""
atexit.register(self.close)
self.port = base_port + worker_id
self._buffer_size = 12000
self._version_ = "API-3"
self._loaded = False
self._open_socket = False
try:
# Establish communication socket
self._socket = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
self._socket.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1)
self._socket.bind(("localhost", self.port))
self._open_socket = True
except socket.error:
self._open_socket = True
self.close()
raise socket.error("Couldn't launch new environment because worker number {} is still in use. "
"You may need to manually close a previously opened environment "
"or use a different worker number.".format(str(worker_id)))
cwd = os.getcwd()
file_name = (file_name.strip()
.replace('.app', '').replace('.exe', '').replace('.x86_64', '').replace('.x86', ''))
true_filename = os.path.basename(os.path.normpath(file_name))
logger.debug('The true file name is {}'.format(true_filename))
launch_string = None
if platform == "linux" or platform == "linux2":
candidates = glob.glob(os.path.join(cwd, file_name) + '.x86_64')
if len(candidates) == 0:
candidates = glob.glob(os.path.join(cwd, file_name) + '.x86')
if len(candidates) == 0:
candidates = glob.glob(file_name + '.x86_64')
if len(candidates) == 0:
candidates = glob.glob(file_name + '.x86')
if len(candidates) > 0:
launch_string = candidates[0]
elif platform == 'darwin':
candidates = glob.glob(os.path.join(cwd, file_name + '.app', 'Contents', 'MacOS', true_filename))
if len(candidates) == 0:
candidates = glob.glob(os.path.join(file_name + '.app', 'Contents', 'MacOS', true_filename))
if len(candidates) == 0:
candidates = glob.glob(os.path.join(cwd, file_name + '.app', 'Contents', 'MacOS', '*'))
if len(candidates) == 0:
candidates = glob.glob(os.path.join(file_name + '.app', 'Contents', 'MacOS', '*'))
if len(candidates) > 0:
launch_string = candidates[0]
elif platform == 'win32':
candidates = glob.glob(os.path.join(cwd, file_name + '.exe'))
if len(candidates) == 0:
candidates = glob.glob(file_name + '.exe')
if len(candidates) > 0:
launch_string = candidates[0]
if launch_string is None:
self.close()
raise UnityEnvironmentException("Couldn't launch the {0} environment. "
"Provided filename does not match any environments."
.format(true_filename))
else:
logger.debug("This is the launch string {}".format(launch_string))
# Launch Unity environment
proc1 = subprocess.Popen(
[launch_string,
'--port', str(self.port),
'--seed', str(seed)])
self._socket.settimeout(30)
try:
try:
self._socket.listen(1)
self._conn, _ = self._socket.accept()
self._conn.settimeout(30)
p = self._conn.recv(self._buffer_size).decode('utf-8')
p = json.loads(p)
except socket.timeout as e:
raise UnityTimeOutException(
"The Unity environment took too long to respond. Make sure {} does not need user interaction to "
"launch and that the Academy and the external Brain(s) are attached to objects in the Scene."
.format(str(file_name)))
if "apiNumber" not in p:
self._unity_version = "API-1"
else:
self._unity_version = p["apiNumber"]
if self._unity_version != self._version_:
raise UnityEnvironmentException(
"The API number is not compatible between Unity and python. Python API : {0}, Unity API : "
"{1}.\nPlease go to https://github.com/Unity-Technologies/ml-agents to download the latest version "
"of ML-Agents.".format(self._version_, self._unity_version))
self._data = {}
self._global_done = None
self._academy_name = p["AcademyName"]
self._log_path = p["logPath"]
# Need to instantiate new AllBrainInfo
self._brains = {}
self._brain_names = p["brainNames"]
self._external_brain_names = p["externalBrainNames"]
self._external_brain_names = [] if self._external_brain_names is None else self._external_brain_names
self._num_brains = len(self._brain_names)
self._num_external_brains = len(self._external_brain_names)
self._resetParameters = p["resetParameters"]
self._curriculum = Curriculum(curriculum, self._resetParameters)
for i in range(self._num_brains):
self._brains[self._brain_names[i]] = BrainParameters(self._brain_names[i], p["brainParameters"][i])
self._loaded = True
logger.info("\n'{0}' started successfully!\n{1}".format(self._academy_name, str(self)))
if self._num_external_brains == 0:
logger.warning(" No External Brains found in the Unity Environment. "
"You will not be able to pass actions to your agent(s).")
except UnityEnvironmentException:
proc1.kill()
self.close()
raise
@property
def curriculum(self):
return self._curriculum
@property
def logfile_path(self):
return self._log_path
@property
def brains(self):
return self._brains
@property
def global_done(self):
return self._global_done
@property
def academy_name(self):
return self._academy_name
@property
def number_brains(self):
return self._num_brains
@property
def number_external_brains(self):
return self._num_external_brains
@property
def brain_names(self):
return self._brain_names
@property
def external_brain_names(self):
return self._external_brain_names
@staticmethod
def _process_pixels(image_bytes=None, bw=False):
"""
Converts byte array observation image into numpy array, re-sizes it, and optionally converts it to grey scale
:param image_bytes: input byte array corresponding to image
:return: processed numpy array of observation from environment
"""
s = bytearray(image_bytes)
image = Image.open(io.BytesIO(s))
s = np.array(image) / 255.0
if bw:
s = np.mean(s, axis=2)
s = np.reshape(s, [s.shape[0], s.shape[1], 1])
return s
def __str__(self):
_new_reset_param = self._curriculum.get_config()
for k in _new_reset_param:
self._resetParameters[k] = _new_reset_param[k]
return '''Unity Academy name: {0}
Number of Brains: {1}
Number of External Brains : {2}
Lesson number : {3}
Reset Parameters :\n\t\t{4}'''.format(self._academy_name, str(self._num_brains),
str(self._num_external_brains), self._curriculum.get_lesson_number,
"\n\t\t".join([str(k) + " -> " + str(self._resetParameters[k])
for k in self._resetParameters])) + '\n' + \
'\n'.join([str(self._brains[b]) for b in self._brains])
def _recv_bytes(self):
try:
s = self._conn.recv(self._buffer_size)
message_length = struct.unpack("I", bytearray(s[:4]))[0]
s = s[4:]
while len(s) != message_length:
s += self._conn.recv(self._buffer_size)
except socket.timeout as e:
raise UnityTimeOutException("The environment took too long to respond.", self._log_path)
return s
def _get_state_image(self, bw):
"""
Receives observation from socket, and confirms.
:param bw:
:return:
"""
s = self._recv_bytes()
s = self._process_pixels(image_bytes=s, bw=bw)
self._conn.send(b"RECEIVED")
return s
def _get_state_dict(self):
"""
Receives dictionary of state information from socket, and confirms.
:return:
"""
state = self._recv_bytes().decode('utf-8')
if state[:14] == "END_OF_MESSAGE":
return {}, state[15:] == 'True'
self._conn.send(b"RECEIVED")
state_dict = json.loads(state)
return state_dict, None
def reset(self, train_mode=True, config=None, lesson=None) -> AllBrainInfo:
"""
Sends a signal to reset the unity environment.
:return: AllBrainInfo : A Data structure corresponding to the initial reset state of the environment.
"""
if config is None:
config = self._curriculum.get_config(lesson)
elif config != {}:
logger.info("\nAcademy Reset with parameters : \t{0}"
.format(', '.join([str(x) + ' -> ' + str(config[x]) for x in config])))
for k in config:
if (k in self._resetParameters) and (isinstance(config[k], (int, float))):
self._resetParameters[k] = config[k]
elif not isinstance(config[k], (int, float)):
raise UnityEnvironmentException(
"The value for parameter '{0}'' must be an Integer or a Float.".format(k))
else:
raise UnityEnvironmentException("The parameter '{0}' is not a valid parameter.".format(k))
if self._loaded:
self._conn.send(b"RESET")
try:
self._conn.recv(self._buffer_size)
except socket.timeout as e:
raise UnityTimeOutException("The environment took too long to respond.", self._log_path)
self._conn.send(json.dumps({"train_model": train_mode, "parameters": config}).encode('utf-8'))
return self._get_state()
else:
raise UnityEnvironmentException("No Unity environment is loaded.")
def _get_state(self) -> AllBrainInfo:
"""
Collects experience information from all external brains in environment at current step.
:return: a dictionary of BrainInfo objects.
"""
self._data = {}
while True:
state_dict, end_of_message = self._get_state_dict()
if end_of_message is not None:
self._global_done = end_of_message
for _b in self._brain_names:
if _b not in self._data:
self._data[_b] = BrainInfo([], np.array([]), [], np.array([]),
[], [], [], np.array([]), [], max_reached=[])
return self._data
b = state_dict["brain_name"]
n_agent = len(state_dict["agents"])
try:
if self._brains[b].vector_observation_space_type == "continuous":
vector_obs = np.array(state_dict["vectorObservations"]).reshape(
(n_agent, self._brains[b].vector_observation_space_size
* self._brains[b].num_stacked_vector_observations))
else:
vector_obs = np.array(state_dict["vectorObservations"]).reshape(
(n_agent, self._brains[b].num_stacked_vector_observations))
except UnityActionException:
raise UnityActionException("Brain {0} has an invalid vector observation. "
"Expecting {1} {2} vector observations but received {3}."
.format(b, n_agent if self._brains[b].vector_observation_space_type == "discrete"
else str(self._brains[b].vector_observation_space_size * n_agent
* self._brains[b].num_stacked_vector_observations),
self._brains[b].vector_observation_space_type,
len(state_dict["vectorObservations"])))
memories = np.array(state_dict["memories"]).reshape((n_agent, -1))
text_obs = state_dict["textObservations"]
rewards = state_dict["rewards"]
dones = state_dict["dones"]
agents = state_dict["agents"]
maxes = state_dict["maxes"]
if n_agent > 0:
vector_actions = np.array(state_dict["previousVectorActions"]).reshape((n_agent, -1))
text_actions = state_dict["previousTextActions"]
else:
vector_actions = np.array([])
text_actions = []
observations = []
for o in range(self._brains[b].number_visual_observations):
obs_n = []
for a in range(n_agent):
obs_n.append(self._get_state_image(self._brains[b].camera_resolutions[o]['blackAndWhite']))
observations.append(np.array(obs_n))
self._data[b] = BrainInfo(observations, vector_obs, text_obs, memories, rewards,
agents, dones, vector_actions, text_actions, max_reached=maxes)
def _send_action(self, vector_action ,memory, text_action):
"""
Send dictionary of actions, memories, and value estimates over socket.
:param vector_action: a dictionary of lists of vector actions.
:param memory: a dictionary of lists of of memories.
:param text_action: a dictionary of lists of text actions.
"""
try:
self._conn.recv(self._buffer_size)
except socket.timeout as e:
raise UnityTimeOutException("The environment took too long to respond.", self._log_path)
action_message = {"vector_action": vector_action, "memory": memory, "text_action": text_action}
self._conn.send(self._append_length(json.dumps(action_message).encode('utf-8')))
@staticmethod
def _append_length(message):
return struct.pack("I", len(message)) + message
@staticmethod
def _flatten(arr):
"""
Converts dictionary of arrays to list for transmission over socket.
:param arr: numpy vector.
:return: flattened list.
"""
if isinstance(arr, (int, np.int_, float, np.float_)):
arr = [float(arr)]
if isinstance(arr, np.ndarray):
arr = arr.tolist()
if len(arr) == 0:
return arr
if isinstance(arr[0], np.ndarray):
arr = [item for sublist in arr for item in sublist.tolist()]
if isinstance(arr[0], list):
arr = [item for sublist in arr for item in sublist]
arr = [float(x) for x in arr]
return arr
def step(self, vector_action=None, memory=None, text_action=None) -> AllBrainInfo:
"""
Provides the environment with an action, moves the environment dynamics forward accordingly, and returns
observation, state, and reward information to the agent.
:param vector_action: Agent's vector action to send to environment. Can be a scalar or vector of int/floats.
:param memory: Vector corresponding to memory used for RNNs, frame-stacking, or other auto-regressive process.
:param text_action: Text action to send to environment for.
:return: AllBrainInfo : A Data structure corresponding to the new state of the environment.
"""
vector_action = {} if vector_action is None else vector_action
memory = {} if memory is None else memory
text_action = {} if text_action is None else text_action
if self._loaded and not self._global_done and self._global_done is not None:
if isinstance(vector_action, (int, np.int_, float, np.float_, list, np.ndarray)):
if self._num_external_brains == 1:
vector_action = {self._external_brain_names[0]: vector_action}
elif self._num_external_brains > 1:
raise UnityActionException(
"You have {0} brains, you need to feed a dictionary of brain names a keys, "
"and vector_actions as values".format(self._num_brains))
else:
raise UnityActionException(
"There are no external brains in the environment, "
"step cannot take a vector_action input")
if isinstance(memory, (int, np.int_, float, np.float_, list, np.ndarray)):
if self._num_external_brains == 1:
memory = {self._external_brain_names[0]: memory}
elif self._num_external_brains > 1:
raise UnityActionException(
"You have {0} brains, you need to feed a dictionary of brain names as keys "
"and memories as values".format(self._num_brains))
else:
raise UnityActionException(
"There are no external brains in the environment, "
"step cannot take a memory input")
if isinstance(text_action, (str, list, np.ndarray)):
if self._num_external_brains == 1:
text_action = {self._external_brain_names[0]: text_action}
elif self._num_external_brains > 1:
raise UnityActionException(
"You have {0} brains, you need to feed a dictionary of brain names as keys "
"and text_actions as values".format(self._num_brains))
else:
raise UnityActionException(
"There are no external brains in the environment, "
"step cannot take a value input")
for brain_name in list(vector_action.keys()) + list(memory.keys()) + list(text_action.keys()):
if brain_name not in self._external_brain_names:
raise UnityActionException(
"The name {0} does not correspond to an external brain "
"in the environment".format(brain_name))
for b in self._external_brain_names:
n_agent = len(self._data[b].agents)
if b not in vector_action:
# raise UnityActionException("You need to input an action for the brain {0}".format(b))
if self._brains[b].vector_action_space_type == "discrete":
vector_action[b] = [0.0] * n_agent
else:
vector_action[b] = [0.0] * n_agent * self._brains[b].vector_action_space_size
else:
vector_action[b] = self._flatten(vector_action[b])
if b not in memory:
memory[b] = []
else:
if memory[b] is None:
memory[b] = []
else:
memory[b] = self._flatten(memory[b])
if b not in text_action:
text_action[b] = [""] * n_agent
else:
if text_action[b] is None:
text_action[b] = []
if isinstance(text_action[b], str):
text_action[b] = [text_action[b]] * n_agent
if not ((len(text_action[b]) == n_agent) or len(text_action[b]) == 0):
raise UnityActionException(
"There was a mismatch between the provided text_action and environment's expectation: "
"The brain {0} expected {1} text_action but was given {2}".format(
b, n_agent, len(text_action[b])))
if not ((self._brains[b].vector_action_space_type == "discrete" and len(vector_action[b]) == n_agent) or
(self._brains[b].vector_action_space_type == "continuous" and len(
vector_action[b]) == self._brains[b].vector_action_space_size * n_agent)):
raise UnityActionException(
"There was a mismatch between the provided action and environment's expectation: "
"The brain {0} expected {1} {2} action(s), but was provided: {3}"
.format(b, n_agent if self._brains[b].vector_action_space_type == "discrete" else
str(self._brains[b].vector_action_space_size * n_agent),
self._brains[b].vector_action_space_type,
str(vector_action[b])))
self._conn.send(b"STEP")
self._send_action(vector_action, memory, text_action)
return self._get_state()
elif not self._loaded:
raise UnityEnvironmentException("No Unity environment is loaded.")
elif self._global_done:
raise UnityActionException("The episode is completed. Reset the environment with 'reset()'")
elif self.global_done is None:
raise UnityActionException(
"You cannot conduct step without first calling reset. Reset the environment with 'reset()'")
def close(self):
"""
Sends a shutdown signal to the unity environment, and closes the socket connection.
"""
if self._loaded & self._open_socket:
self._conn.send(b"EXIT")
self._conn.close()
if self._open_socket:
self._socket.close()
self._loaded = False
else:
raise UnityEnvironmentException("No Unity environment is loaded.")