您最多选择25个主题
主题必须以中文或者字母或数字开头，可以包含连字符 (-)，并且长度不得超过35个字符
361 行
16 KiB
361 行
16 KiB
import atexit
|
|
import io
|
|
import glob
|
|
import json
|
|
import logging
|
|
import numpy as np
|
|
import os
|
|
import socket
|
|
import subprocess
|
|
import signal
|
|
|
|
from .brain import *
|
|
from .exception import *
|
|
|
|
from PIL import Image
|
|
from sys import platform
|
|
|
|
logging.basicConfig(level=logging.INFO)
|
|
logger = logging.getLogger(__name__)
|
|
|
|
|
|
class UnityEnvironment(object):
|
|
def __init__(self, file_name, worker_id=0,
|
|
base_port=5005):
|
|
"""
|
|
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 = 120000
|
|
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()
|
|
try:
|
|
true_filename = os.path.basename(os.path.normpath(file_name))
|
|
launch_string = ""
|
|
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:
|
|
launch_string = candidates[0]
|
|
else:
|
|
raise UnityEnvironmentException("Couldn't launch new environment. Provided filename "
|
|
"does not match any environments in {}. ".format(cwd))
|
|
elif platform == 'darwin':
|
|
launch_string = os.path.join(cwd, file_name + '.app', 'Contents', 'MacOS', true_filename)
|
|
elif platform == 'win32':
|
|
launch_string = os.path.join(cwd, file_name + '.exe')
|
|
|
|
# Launch Unity environment
|
|
proc1 = subprocess.Popen(
|
|
[launch_string,
|
|
'--port', str(self.port)])
|
|
except os.error:
|
|
self.close()
|
|
raise UnityEnvironmentException("Couldn't launch new environment. "
|
|
"Provided filename does not match any \environments in {}."
|
|
.format(cwd))
|
|
|
|
def timeout_handler():
|
|
raise UnityEnvironmentException(
|
|
"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) scripts are attached to objects in the Scene.".format(
|
|
str(file_name)))
|
|
|
|
old_handler = signal.signal(signal.SIGALRM, timeout_handler)
|
|
signal.alarm(30) # trigger alarm in x seconds
|
|
try:
|
|
self._socket.listen(1)
|
|
self._conn, _ = self._socket.accept()
|
|
p = self._conn.recv(self._buffer_size).decode('utf-8')
|
|
p = json.loads(p)
|
|
except UnityEnvironmentException:
|
|
proc1.kill()
|
|
self.close()
|
|
raise
|
|
signal.signal(signal.SIGALRM, old_handler)
|
|
signal.alarm(0)
|
|
|
|
self._data = {}
|
|
self._global_done = None
|
|
self._academy_name = p["AcademyName"]
|
|
self._num_brains = len(p["brainParameters"])
|
|
self._brains = {}
|
|
self._brain_names = p["brainNames"]
|
|
self._resetParameters = p["resetParameters"]
|
|
for i in range(self._num_brains):
|
|
self._brains[self._brain_names[i]] = BrainParameters(self._brain_names[i], p["brainParameters"][i])
|
|
self._conn.send(b".")
|
|
self._loaded = True
|
|
logger.info("\n'{}' started successfully!".format(self._academy_name))
|
|
|
|
@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 brain_names(self):
|
|
return self._brain_names
|
|
|
|
@staticmethod
|
|
def _process_pixels(image_bytes=None, bw=False):
|
|
"""
|
|
Converts bytearray observation image into numpy array, resizes it, and optionally converts it to greyscale
|
|
:param image_bytes: input bytearray 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):
|
|
return '''Unity Academy name: {0}
|
|
Number of brains: {1}
|
|
Reset Parameters :\n\t\t{2}'''.format(self._academy_name, str(self._num_brains),
|
|
"\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 _get_state_image(self, bw):
|
|
"""
|
|
Receives observation from socket, and confirms.
|
|
:param bw:
|
|
:return:
|
|
"""
|
|
s = self._conn.recv(self._buffer_size)
|
|
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._conn.recv(self._buffer_size).decode('utf-8')
|
|
self._conn.send(b"RECEIVED")
|
|
state_dict = json.loads(state)
|
|
return state_dict
|
|
|
|
def reset(self, train_mode=True, config=None):
|
|
"""
|
|
Sends a signal to reset the unity environment.
|
|
:return: A Data structure corresponding to the initial reset state of the environment.
|
|
"""
|
|
config = config or {}
|
|
if self._loaded:
|
|
self._conn.send(b"RESET")
|
|
self._conn.recv(self._buffer_size)
|
|
self._conn.send(json.dumps({"train_model": train_mode, "parameters": config}).encode('utf-8'))
|
|
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))
|
|
|
|
return self._get_state()
|
|
else:
|
|
raise UnityEnvironmentException("No Unity environment is loaded.")
|
|
|
|
def _get_state(self):
|
|
"""
|
|
Collects experience information from all external brains in environment at current step.
|
|
:return: a dictionary BrainInfo objects.
|
|
"""
|
|
self._data = {}
|
|
for index in range(self._num_brains):
|
|
state_dict = self._get_state_dict()
|
|
b = state_dict["brain_name"]
|
|
n_agent = len(state_dict["agents"])
|
|
try:
|
|
if self._brains[b].state_space_type == "continuous":
|
|
states = np.array(state_dict["states"]).reshape((n_agent, self._brains[b].state_space_size))
|
|
else:
|
|
states = np.array(state_dict["states"]).reshape((n_agent, 1))
|
|
except UnityActionException:
|
|
raise UnityActionException("Brain {0} has an invalid state. "
|
|
"Expecting {1} {2} state but received {3}."
|
|
.format(b, n_agent if self._brains[b].state_space_type == "discrete"
|
|
else str(self._brains[b].state_space_size * n_agent),
|
|
self._brains[b].state_space_type,
|
|
len(state_dict["states"])))
|
|
memories = np.array(state_dict["memories"]).reshape((n_agent, self._brains[b].memory_space_size))
|
|
rewards = state_dict["rewards"]
|
|
dones = state_dict["dones"]
|
|
agents = state_dict["agents"]
|
|
|
|
observations = []
|
|
for o in range(self._brains[b].number_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, states, memories, rewards, agents, dones)
|
|
|
|
self._global_done = self._conn.recv(self._buffer_size).decode('utf-8') == 'True'
|
|
|
|
return self._data
|
|
|
|
def _send_action(self, action, memory, value):
|
|
"""
|
|
Send dictionary of actions, memories, and value estimates over socket.
|
|
:param action: a dictionary of lists of actions.
|
|
:param memory: a dictionary of lists of of memories.
|
|
:param value: a dictionary of lists of of value estimates.
|
|
"""
|
|
self._conn.recv(self._buffer_size)
|
|
action_message = {"action": action, "memory": memory, "value": value}
|
|
self._conn.send(json.dumps(action_message).encode('utf-8'))
|
|
|
|
@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, action, memory=None, value=None):
|
|
"""
|
|
Provides the environment with an action, moves the environment dynamics forward accordingly, and returns
|
|
observation, state, and reward information to the agent.
|
|
:param action: Agent's 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 value: Value estimate to send to environment for visualization. Can be a scalar or vector of float(s).
|
|
:return: A Data structure corresponding to the new state of the environment.
|
|
"""
|
|
memory = {} if memory is None else memory
|
|
value = {} if value is None else value
|
|
if self._loaded and not self._global_done and self._global_done is not None:
|
|
if isinstance(action, (int, np.int_, float, np.float_, list, np.ndarray)):
|
|
if self._num_brains > 1:
|
|
raise UnityActionException(
|
|
"You have {0} brains, you need to feed a dictionary of brain names a keys, "
|
|
"and actions as values".format(self._num_brains))
|
|
else:
|
|
action = {self._brain_names[0]: action}
|
|
if isinstance(memory, (int, np.int_, float, np.float_, list, np.ndarray)):
|
|
if self._num_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:
|
|
memory = {self._brain_names[0]: memory}
|
|
if isinstance(value, (int, np.int_, float, np.float_, list, np.ndarray)):
|
|
if self._num_brains > 1:
|
|
raise UnityActionException(
|
|
"You have {0} brains, you need to feed a dictionary of brain names as keys "
|
|
"and state/action value estimates as values".format(self._num_brains))
|
|
else:
|
|
value = {self._brain_names[0]: value}
|
|
|
|
for b in self._brain_names:
|
|
n_agent = len(self._data[b].agents)
|
|
if b not in action:
|
|
raise UnityActionException("You need to input an action for the brain {0}".format(b))
|
|
action[b] = self._flatten(action[b])
|
|
if b not in memory:
|
|
memory[b] = [0.0] * self._brains[b].memory_space_size * n_agent
|
|
else:
|
|
memory[b] = self._flatten(memory[b])
|
|
if b not in value:
|
|
value[b] = [0.0] * n_agent
|
|
else:
|
|
value[b] = self._flatten(value[b])
|
|
if not (len(value[b]) == n_agent):
|
|
raise UnityActionException(
|
|
"There was a mismatch between the provided value and environment's expectation: "
|
|
"The brain {0} expected {1} value but was given {2}".format(b, n_agent, len(value[b])))
|
|
if not (len(memory[b]) == self._brains[b].memory_space_size * n_agent):
|
|
raise UnityActionException(
|
|
"There was a mismatch between the provided memory and environment's expectation: "
|
|
"The brain {0} expected {1} memories but was given {2}"
|
|
.format(b, self._brains[b].memory_space_size * n_agent, len(memory[b])))
|
|
if not ((self._brains[b].action_space_type == "discrete" and len(action[b]) == n_agent) or
|
|
(self._brains[b].action_space_type == "continuous" and len(
|
|
action[b]) == self._brains[b].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].action_space_type == "discrete" else
|
|
str(self._brains[b].action_space_size * n_agent), self._brains[b].action_space_type,
|
|
str(action[b])))
|
|
self._conn.send(b"STEP")
|
|
self._send_action(action, memory, value)
|
|
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.")
|