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509 行
24 KiB
509 行
24 KiB
import atexit
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import glob
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import io
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import logging
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import numpy as np
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import os
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import subprocess
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from .brain import BrainInfo, BrainParameters, AllBrainInfo
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from .exception import UnityEnvironmentException, UnityActionException, UnityTimeOutException
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from .curriculum import Curriculum
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from communicator_objects import UnityRLInput, UnityRLOutput, AgentActionProto,\
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EnvironmentParametersProto, UnityRLInitializationInput, UnityRLInitializationOutput,\
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UnityInput, UnityOutput
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from .rpc_communicator import RpcCommunicator
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from .socket_communicator import SocketCommunicator
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from sys import platform
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from PIL import Image
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logging.basicConfig(level=logging.INFO)
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logger = logging.getLogger("unityagents")
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class UnityEnvironment(object):
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def __init__(self, file_name=None, worker_id=0,
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base_port=5005, curriculum=None,
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seed=0, docker_training=False, no_graphics=False):
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"""
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Starts a new unity environment and establishes a connection with the environment.
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Notice: Currently communication between Unity and Python takes place over an open socket without authentication.
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Ensure that the network where training takes place is secure.
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:string file_name: Name of Unity environment binary.
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:int base_port: Baseline port number to connect to Unity environment over. worker_id increments over this.
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:int worker_id: Number to add to communication port (5005) [0]. Used for asynchronous agent scenarios.
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:param docker_training: Informs this class whether the process is being run within a container.
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:param no_graphics: Whether to run the Unity simulator in no-graphics mode
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"""
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atexit.register(self._close)
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self.port = base_port + worker_id
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self._buffer_size = 12000
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self._version_ = "API-4"
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self._loaded = False # If true, this means the environment was successfully loaded
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self.proc1 = None # The process that is started. If None, no process was started
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self.communicator = self.get_communicator(worker_id, base_port)
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# If the environment name is 'editor', a new environment will not be launched
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# and the communicator will directly try to connect to an existing unity environment.
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if file_name is not None:
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self.executable_launcher(file_name, docker_training, no_graphics)
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else:
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logger.info("Start training by pressing the Play button in the Unity Editor.")
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self._loaded = True
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rl_init_parameters_in = UnityRLInitializationInput(
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seed=seed
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)
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try:
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aca_params = self.send_academy_parameters(rl_init_parameters_in)
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except UnityTimeOutException:
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self._close()
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raise
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# TODO : think of a better way to expose the academyParameters
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self._unity_version = aca_params.version
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if self._unity_version != self._version_:
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raise UnityEnvironmentException(
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"The API number is not compatible between Unity and python. Python API : {0}, Unity API : "
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"{1}.\nPlease go to https://github.com/Unity-Technologies/ml-agents to download the latest version "
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"of ML-Agents.".format(self._version_, self._unity_version))
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self._n_agents = {}
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self._global_done = None
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self._academy_name = aca_params.name
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self._log_path = aca_params.log_path
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self._brains = {}
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self._brain_names = []
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self._external_brain_names = []
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for brain_param in aca_params.brain_parameters:
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self._brain_names += [brain_param.brain_name]
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resolution = [{
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"height": x.height,
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"width": x.width,
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"blackAndWhite": x.gray_scale
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} for x in brain_param.camera_resolutions]
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self._brains[brain_param.brain_name] = \
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BrainParameters(brain_param.brain_name, {
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"vectorObservationSize": brain_param.vector_observation_size,
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"numStackedVectorObservations": brain_param.num_stacked_vector_observations,
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"cameraResolutions": resolution,
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"vectorActionSize": brain_param.vector_action_size,
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"vectorActionDescriptions": brain_param.vector_action_descriptions,
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"vectorActionSpaceType": brain_param.vector_action_space_type
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})
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if brain_param.brain_type == 2:
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self._external_brain_names += [brain_param.brain_name]
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self._num_brains = len(self._brain_names)
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self._num_external_brains = len(self._external_brain_names)
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self._resetParameters = dict(aca_params.environment_parameters.float_parameters) # TODO
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self._curriculum = Curriculum(curriculum, self._resetParameters)
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logger.info("\n'{0}' started successfully!\n{1}".format(self._academy_name, str(self)))
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if self._num_external_brains == 0:
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logger.warning(" No External Brains found in the Unity Environment. "
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"You will not be able to pass actions to your agent(s).")
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@property
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def curriculum(self):
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return self._curriculum
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@property
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def logfile_path(self):
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return self._log_path
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@property
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def brains(self):
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return self._brains
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@property
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def global_done(self):
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return self._global_done
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@property
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def academy_name(self):
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return self._academy_name
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@property
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def number_brains(self):
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return self._num_brains
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@property
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def number_external_brains(self):
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return self._num_external_brains
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@property
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def brain_names(self):
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return self._brain_names
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@property
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def external_brain_names(self):
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return self._external_brain_names
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def executable_launcher(self, file_name, docker_training, no_graphics):
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cwd = os.getcwd()
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file_name = (file_name.strip()
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.replace('.app', '').replace('.exe', '').replace('.x86_64', '').replace('.x86', ''))
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true_filename = os.path.basename(os.path.normpath(file_name))
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logger.debug('The true file name is {}'.format(true_filename))
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launch_string = None
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if platform == "linux" or platform == "linux2":
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candidates = glob.glob(os.path.join(cwd, file_name) + '.x86_64')
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if len(candidates) == 0:
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candidates = glob.glob(os.path.join(cwd, file_name) + '.x86')
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if len(candidates) == 0:
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candidates = glob.glob(file_name + '.x86_64')
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if len(candidates) == 0:
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candidates = glob.glob(file_name + '.x86')
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if len(candidates) > 0:
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launch_string = candidates[0]
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elif platform == 'darwin':
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candidates = glob.glob(os.path.join(cwd, file_name + '.app', 'Contents', 'MacOS', true_filename))
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if len(candidates) == 0:
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candidates = glob.glob(os.path.join(file_name + '.app', 'Contents', 'MacOS', true_filename))
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if len(candidates) == 0:
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candidates = glob.glob(os.path.join(cwd, file_name + '.app', 'Contents', 'MacOS', '*'))
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if len(candidates) == 0:
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candidates = glob.glob(os.path.join(file_name + '.app', 'Contents', 'MacOS', '*'))
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if len(candidates) > 0:
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launch_string = candidates[0]
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elif platform == 'win32':
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candidates = glob.glob(os.path.join(cwd, file_name + '.exe'))
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if len(candidates) == 0:
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candidates = glob.glob(file_name + '.exe')
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if len(candidates) > 0:
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launch_string = candidates[0]
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if launch_string is None:
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self._close()
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raise UnityEnvironmentException("Couldn't launch the {0} environment. "
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"Provided filename does not match any environments."
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.format(true_filename))
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else:
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logger.debug("This is the launch string {}".format(launch_string))
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# Launch Unity environment
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if not docker_training:
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if no_graphics:
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self.proc1 = subprocess.Popen(
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[launch_string,'-nographics', '-batchmode',
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'--port', str(self.port)])
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else:
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self.proc1 = subprocess.Popen(
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[launch_string, '--port', str(self.port)])
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else:
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"""
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Comments for future maintenance:
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xvfb-run is a wrapper around Xvfb, a virtual xserver where all
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rendering is done to virtual memory. It automatically creates a
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new virtual server automatically picking a server number `auto-servernum`.
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The server is passed the arguments using `server-args`, we are telling
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Xvfb to create Screen number 0 with width 640, height 480 and depth 24 bits.
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Note that 640 X 480 are the default width and height. The main reason for
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us to add this is because we'd like to change the depth from the default
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of 8 bits to 24.
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Unfortunately, this means that we will need to pass the arguments through
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a shell which is why we set `shell=True`. Now, this adds its own
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complications. E.g SIGINT can bounce off the shell and not get propagated
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to the child processes. This is why we add `exec`, so that the shell gets
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launched, the arguments are passed to `xvfb-run`. `exec` replaces the shell
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we created with `xvfb`.
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"""
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docker_ls = ("exec xvfb-run --auto-servernum"
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" --server-args='-screen 0 640x480x24'"
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" {0} --port {1}").format(launch_string, str(self.port))
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self.proc1 = subprocess.Popen(docker_ls,
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stdout=subprocess.PIPE,
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stderr=subprocess.PIPE,
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shell=True)
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def get_communicator(self, worker_id, base_port):
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return RpcCommunicator(worker_id, base_port)
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# return SocketCommunicator(worker_id, base_port)
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def __str__(self):
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_new_reset_param = self._curriculum.get_config()
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for k in _new_reset_param:
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self._resetParameters[k] = _new_reset_param[k]
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return '''Unity Academy name: {0}
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Number of Brains: {1}
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Number of External Brains : {2}
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Lesson number : {3}
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Reset Parameters :\n\t\t{4}'''.format(self._academy_name, str(self._num_brains),
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str(self._num_external_brains), self._curriculum.get_lesson_number,
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"\n\t\t".join([str(k) + " -> " + str(self._resetParameters[k])
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for k in self._resetParameters])) + '\n' + \
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'\n'.join([str(self._brains[b]) for b in self._brains])
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def reset(self, train_mode=True, config=None, lesson=None) -> AllBrainInfo:
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"""
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Sends a signal to reset the unity environment.
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:return: AllBrainInfo : A Data structure corresponding to the initial reset state of the environment.
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"""
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if config is None:
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config = self._curriculum.get_config(lesson)
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elif config != {}:
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logger.info("\nAcademy Reset with parameters : \t{0}"
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.format(', '.join([str(x) + ' -> ' + str(config[x]) for x in config])))
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for k in config:
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if (k in self._resetParameters) and (isinstance(config[k], (int, float))):
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self._resetParameters[k] = config[k]
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elif not isinstance(config[k], (int, float)):
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raise UnityEnvironmentException(
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"The value for parameter '{0}'' must be an Integer or a Float.".format(k))
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else:
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raise UnityEnvironmentException("The parameter '{0}' is not a valid parameter.".format(k))
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if self._loaded:
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outputs = self.communicator.exchange(
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self._generate_reset_input(train_mode, config)
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)
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if outputs is None:
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raise KeyboardInterrupt
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rl_output = outputs.rl_output
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s = self._get_state(rl_output)
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self._global_done = s[1]
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for _b in self._external_brain_names:
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self._n_agents[_b] = len(s[0][_b].agents)
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return s[0]
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else:
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raise UnityEnvironmentException("No Unity environment is loaded.")
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def step(self, vector_action=None, memory=None, text_action=None) -> AllBrainInfo:
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"""
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Provides the environment with an action, moves the environment dynamics forward accordingly, and returns
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observation, state, and reward information to the agent.
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:param vector_action: Agent's vector action to send to environment. Can be a scalar or vector of int/floats.
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:param memory: Vector corresponding to memory used for RNNs, frame-stacking, or other auto-regressive process.
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:param text_action: Text action to send to environment for.
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:return: AllBrainInfo : A Data structure corresponding to the new state of the environment.
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"""
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vector_action = {} if vector_action is None else vector_action
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memory = {} if memory is None else memory
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text_action = {} if text_action is None else text_action
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if self._loaded and not self._global_done and self._global_done is not None:
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if isinstance(vector_action, (int, np.int_, float, np.float_, list, np.ndarray)):
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if self._num_external_brains == 1:
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vector_action = {self._external_brain_names[0]: vector_action}
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elif self._num_external_brains > 1:
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raise UnityActionException(
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"You have {0} brains, you need to feed a dictionary of brain names a keys, "
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"and vector_actions as values".format(self._num_brains))
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else:
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raise UnityActionException(
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"There are no external brains in the environment, "
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"step cannot take a vector_action input")
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if isinstance(memory, (int, np.int_, float, np.float_, list, np.ndarray)):
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if self._num_external_brains == 1:
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memory = {self._external_brain_names[0]: memory}
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elif self._num_external_brains > 1:
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raise UnityActionException(
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"You have {0} brains, you need to feed a dictionary of brain names as keys "
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"and memories as values".format(self._num_brains))
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else:
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raise UnityActionException(
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"There are no external brains in the environment, "
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"step cannot take a memory input")
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if isinstance(text_action, (str, list, np.ndarray)):
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if self._num_external_brains == 1:
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text_action = {self._external_brain_names[0]: text_action}
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elif self._num_external_brains > 1:
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raise UnityActionException(
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"You have {0} brains, you need to feed a dictionary of brain names as keys "
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"and text_actions as values".format(self._num_brains))
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else:
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raise UnityActionException(
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"There are no external brains in the environment, "
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"step cannot take a value input")
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for brain_name in list(vector_action.keys()) + list(memory.keys()) + list(text_action.keys()):
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if brain_name not in self._external_brain_names:
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raise UnityActionException(
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"The name {0} does not correspond to an external brain "
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"in the environment".format(brain_name))
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for b in self._external_brain_names:
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n_agent = self._n_agents[b]
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if b not in vector_action:
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# raise UnityActionException("You need to input an action for the brain {0}".format(b))
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if self._brains[b].vector_action_space_type == "discrete":
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vector_action[b] = [0.0] * n_agent
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else:
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vector_action[b] = [0.0] * n_agent * self._brains[b].vector_action_space_size
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else:
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vector_action[b] = self._flatten(vector_action[b])
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if b not in memory:
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memory[b] = []
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else:
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if memory[b] is None:
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memory[b] = []
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else:
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memory[b] = self._flatten(memory[b])
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if b not in text_action:
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text_action[b] = [""] * n_agent
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else:
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if text_action[b] is None:
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text_action[b] = [""] * n_agent
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if isinstance(text_action[b], str):
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text_action[b] = [text_action[b]] * n_agent
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if not ((len(text_action[b]) == n_agent) or len(text_action[b]) == 0):
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raise UnityActionException(
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"There was a mismatch between the provided text_action and environment's expectation: "
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"The brain {0} expected {1} text_action but was given {2}".format(
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b, n_agent, len(text_action[b])))
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if not ((self._brains[b].vector_action_space_type == "discrete" and len(vector_action[b]) == n_agent) or
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(self._brains[b].vector_action_space_type == "continuous" and len(
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vector_action[b]) == self._brains[b].vector_action_space_size * n_agent)):
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raise UnityActionException(
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"There was a mismatch between the provided action and environment's expectation: "
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"The brain {0} expected {1} {2} action(s), but was provided: {3}"
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.format(b, n_agent if self._brains[b].vector_action_space_type == "discrete" else
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str(self._brains[b].vector_action_space_size * n_agent),
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self._brains[b].vector_action_space_type,
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str(vector_action[b])))
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outputs = self.communicator.exchange(
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self._generate_step_input(vector_action, memory, text_action)
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)
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if outputs is None:
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raise KeyboardInterrupt
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rl_output = outputs.rl_output
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s = self._get_state(rl_output)
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self._global_done = s[1]
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for _b in self._external_brain_names:
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self._n_agents[_b] = len(s[0][_b].agents)
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return s[0]
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elif not self._loaded:
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raise UnityEnvironmentException("No Unity environment is loaded.")
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elif self._global_done:
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raise UnityActionException("The episode is completed. Reset the environment with 'reset()'")
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elif self.global_done is None:
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raise UnityActionException(
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"You cannot conduct step without first calling reset. Reset the environment with 'reset()'")
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def close(self):
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"""
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Sends a shutdown signal to the unity environment, and closes the socket connection.
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"""
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if self._loaded:
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self._close()
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else:
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raise UnityEnvironmentException("No Unity environment is loaded.")
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def _close(self):
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self._loaded = False
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self.communicator.close()
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if self.proc1 is not None:
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self.proc1.kill()
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@staticmethod
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def _flatten(arr):
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"""
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Converts arrays to list.
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:param arr: numpy vector.
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:return: flattened list.
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"""
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if isinstance(arr, (int, np.int_, float, np.float_)):
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arr = [float(arr)]
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if isinstance(arr, np.ndarray):
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arr = arr.tolist()
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if len(arr) == 0:
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return arr
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if isinstance(arr[0], np.ndarray):
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arr = [item for sublist in arr for item in sublist.tolist()]
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if isinstance(arr[0], list):
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arr = [item for sublist in arr for item in sublist]
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arr = [float(x) for x in arr]
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return arr
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@staticmethod
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def _process_pixels(image_bytes, gray_scale):
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"""
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Converts byte array observation image into numpy array, re-sizes it, and optionally converts it to grey scale
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:param image_bytes: input byte array corresponding to image
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:return: processed numpy array of observation from environment
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"""
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s = bytearray(image_bytes)
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image = Image.open(io.BytesIO(s))
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s = np.array(image) / 255.0
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if gray_scale:
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s = np.mean(s, axis=2)
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s = np.reshape(s, [s.shape[0], s.shape[1], 1])
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return s
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def _get_state(self, output: UnityRLOutput) -> (AllBrainInfo, bool):
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"""
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Collects experience information from all external brains in environment at current step.
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:return: a dictionary of BrainInfo objects.
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"""
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_data = {}
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global_done = output.global_done
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|
for b in output.agentInfos:
|
|
agent_info_list = output.agentInfos[b].value
|
|
vis_obs = []
|
|
for i in range(self.brains[b].number_visual_observations):
|
|
obs = [self._process_pixels(x.visual_observations[i],
|
|
self.brains[b].camera_resolutions[i]['blackAndWhite'])
|
|
for x in agent_info_list]
|
|
vis_obs += [np.array(obs)]
|
|
if len(agent_info_list) == 0:
|
|
memory_size = 0
|
|
else:
|
|
memory_size = max([len(x.memories) for x in agent_info_list])
|
|
if memory_size == 0:
|
|
memory = np.zeros((0, 0))
|
|
else:
|
|
[x.memories.extend([0] * (memory_size - len(x.memories))) for x in agent_info_list]
|
|
memory = np.array([x.memories for x in agent_info_list])
|
|
_data[b] = BrainInfo(
|
|
visual_observation=vis_obs,
|
|
vector_observation=np.array([x.stacked_vector_observation for x in agent_info_list]),
|
|
text_observations=[x.text_observation for x in agent_info_list],
|
|
memory=memory,
|
|
reward=[x.reward for x in agent_info_list],
|
|
agents=[x.id for x in agent_info_list],
|
|
local_done=[x.done for x in agent_info_list],
|
|
vector_action=np.array([x.stored_vector_actions for x in agent_info_list]),
|
|
text_action=[x.stored_text_actions for x in agent_info_list],
|
|
max_reached=[x.max_step_reached for x in agent_info_list]
|
|
)
|
|
return _data, global_done
|
|
|
|
def _generate_step_input(self, vector_action, memory, text_action) -> UnityRLInput:
|
|
rl_in = UnityRLInput()
|
|
for b in vector_action:
|
|
n_agents = self._n_agents[b]
|
|
if n_agents == 0:
|
|
continue
|
|
_a_s = len(vector_action[b]) // n_agents
|
|
_m_s = len(memory[b]) // n_agents
|
|
for i in range(n_agents):
|
|
action = AgentActionProto(
|
|
vector_actions=vector_action[b][i*_a_s: (i+1)*_a_s],
|
|
memories=memory[b][i*_m_s: (i+1)*_m_s],
|
|
text_actions=text_action[b][i]
|
|
)
|
|
rl_in.agent_actions[b].value.extend([action])
|
|
rl_in.command = 0
|
|
return self.wrap_unity_input(rl_in)
|
|
|
|
def _generate_reset_input(self, training, config) -> UnityRLInput:
|
|
rl_in = UnityRLInput()
|
|
rl_in.is_training = training
|
|
rl_in.environment_parameters.CopyFrom(EnvironmentParametersProto())
|
|
for key in config:
|
|
rl_in.environment_parameters.float_parameters[key] = config[key]
|
|
rl_in.command = 1
|
|
return self.wrap_unity_input(rl_in)
|
|
|
|
def send_academy_parameters(self, init_parameters: UnityRLInitializationInput) -> UnityRLInitializationOutput:
|
|
inputs = UnityInput()
|
|
inputs.rl_initialization_input.CopyFrom(init_parameters)
|
|
return self.communicator.initialize(inputs).rl_initialization_output
|
|
|
|
def wrap_unity_input(self, rl_input: UnityRLInput) -> UnityOutput:
|
|
result = UnityInput()
|
|
result.rl_input.CopyFrom(rl_input)
|
|
return result
|