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
517 行
25 KiB
517 行
25 KiB
import atexit
|
|
import glob
|
|
import io
|
|
import logging
|
|
import numpy as np
|
|
import os
|
|
import subprocess
|
|
|
|
from .brain import BrainInfo, BrainParameters, AllBrainInfo
|
|
from .exception import UnityEnvironmentException, UnityActionException, UnityTimeOutException
|
|
|
|
from communicator_objects import UnityRLInput, UnityRLOutput, AgentActionProto,\
|
|
EnvironmentParametersProto, UnityRLInitializationInput, UnityRLInitializationOutput,\
|
|
UnityInput, UnityOutput
|
|
|
|
from .rpc_communicator import RpcCommunicator
|
|
from .socket_communicator import SocketCommunicator
|
|
|
|
|
|
from sys import platform
|
|
from PIL import Image
|
|
|
|
logging.basicConfig(level=logging.INFO)
|
|
logger = logging.getLogger("unityagents")
|
|
|
|
|
|
class UnityEnvironment(object):
|
|
def __init__(self, file_name=None, worker_id=0,
|
|
base_port=5005, seed=0,
|
|
docker_training=False, no_graphics=False):
|
|
"""
|
|
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.
|
|
:param docker_training: Informs this class whether the process is being run within a container.
|
|
:param no_graphics: Whether to run the Unity simulator in no-graphics mode
|
|
"""
|
|
|
|
atexit.register(self._close)
|
|
self.port = base_port + worker_id
|
|
self._buffer_size = 12000
|
|
self._version_ = "API-4"
|
|
self._loaded = False # If true, this means the environment was successfully loaded
|
|
self.proc1 = None # The process that is started. If None, no process was started
|
|
self.communicator = self.get_communicator(worker_id, base_port)
|
|
|
|
# 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, docker_training, no_graphics)
|
|
else:
|
|
logger.info("Start training by pressing the Play button in the Unity Editor.")
|
|
self._loaded = True
|
|
|
|
rl_init_parameters_in = UnityRLInitializationInput(
|
|
seed=seed
|
|
)
|
|
try:
|
|
aca_params = self.send_academy_parameters(rl_init_parameters_in)
|
|
except UnityTimeOutException:
|
|
self._close()
|
|
raise
|
|
# TODO : think of a better way to expose the academyParameters
|
|
self._unity_version = aca_params.version
|
|
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._n_agents = {}
|
|
self._global_done = None
|
|
self._academy_name = aca_params.name
|
|
self._log_path = aca_params.log_path
|
|
self._brains = {}
|
|
self._brain_names = []
|
|
self._external_brain_names = []
|
|
for brain_param in aca_params.brain_parameters:
|
|
self._brain_names += [brain_param.brain_name]
|
|
resolution = [{
|
|
"height": x.height,
|
|
"width": x.width,
|
|
"blackAndWhite": x.gray_scale
|
|
} for x in brain_param.camera_resolutions]
|
|
self._brains[brain_param.brain_name] = \
|
|
BrainParameters(brain_param.brain_name, {
|
|
"vectorObservationSize": brain_param.vector_observation_size,
|
|
"numStackedVectorObservations": brain_param.num_stacked_vector_observations,
|
|
"cameraResolutions": resolution,
|
|
"vectorActionSize": brain_param.vector_action_size,
|
|
"vectorActionDescriptions": brain_param.vector_action_descriptions,
|
|
"vectorActionSpaceType": brain_param.vector_action_space_type
|
|
})
|
|
if brain_param.brain_type == 2:
|
|
self._external_brain_names += [brain_param.brain_name]
|
|
self._num_brains = len(self._brain_names)
|
|
self._num_external_brains = len(self._external_brain_names)
|
|
self._resetParameters = dict(aca_params.environment_parameters.float_parameters) # TODO
|
|
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).")
|
|
|
|
@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
|
|
|
|
def executable_launcher(self, file_name, docker_training, no_graphics):
|
|
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
|
|
if not docker_training:
|
|
if no_graphics:
|
|
self.proc1 = subprocess.Popen(
|
|
[launch_string,'-nographics', '-batchmode',
|
|
'--port', str(self.port)])
|
|
else:
|
|
self.proc1 = subprocess.Popen(
|
|
[launch_string, '--port', str(self.port)])
|
|
else:
|
|
"""
|
|
Comments for future maintenance:
|
|
xvfb-run is a wrapper around Xvfb, a virtual xserver where all
|
|
rendering is done to virtual memory. It automatically creates a
|
|
new virtual server automatically picking a server number `auto-servernum`.
|
|
The server is passed the arguments using `server-args`, we are telling
|
|
Xvfb to create Screen number 0 with width 640, height 480 and depth 24 bits.
|
|
Note that 640 X 480 are the default width and height. The main reason for
|
|
us to add this is because we'd like to change the depth from the default
|
|
of 8 bits to 24.
|
|
Unfortunately, this means that we will need to pass the arguments through
|
|
a shell which is why we set `shell=True`. Now, this adds its own
|
|
complications. E.g SIGINT can bounce off the shell and not get propagated
|
|
to the child processes. This is why we add `exec`, so that the shell gets
|
|
launched, the arguments are passed to `xvfb-run`. `exec` replaces the shell
|
|
we created with `xvfb`.
|
|
"""
|
|
docker_ls = ("exec xvfb-run --auto-servernum"
|
|
" --server-args='-screen 0 640x480x24'"
|
|
" {0} --port {1}").format(launch_string, str(self.port))
|
|
self.proc1 = subprocess.Popen(docker_ls,
|
|
stdout=subprocess.PIPE,
|
|
stderr=subprocess.PIPE,
|
|
shell=True)
|
|
|
|
def get_communicator(self, worker_id, base_port):
|
|
return RpcCommunicator(worker_id, base_port)
|
|
# return SocketCommunicator(worker_id, base_port)
|
|
|
|
def __str__(self):
|
|
return '''Unity Academy name: {0}
|
|
Number of Brains: {1}
|
|
Number of External Brains : {2}
|
|
Reset Parameters :\n\t\t{3}'''.format(self._academy_name, str(self._num_brains),
|
|
str(self._num_external_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 reset(self, config=None, train_mode=True) -> 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._resetParameters
|
|
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:
|
|
outputs = self.communicator.exchange(
|
|
self._generate_reset_input(train_mode, config)
|
|
)
|
|
if outputs is None:
|
|
raise KeyboardInterrupt
|
|
rl_output = outputs.rl_output
|
|
s = self._get_state(rl_output)
|
|
self._global_done = s[1]
|
|
for _b in self._external_brain_names:
|
|
self._n_agents[_b] = len(s[0][_b].agents)
|
|
return s[0]
|
|
else:
|
|
raise UnityEnvironmentException("No Unity environment is loaded.")
|
|
|
|
def step(self, vector_action=None, memory=None, text_action=None, value=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
|
|
value = {} if value is None else value
|
|
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")
|
|
if isinstance(value, (int, np.int_, float, np.float_, list, np.ndarray)):
|
|
if self._num_external_brains == 1:
|
|
value = {self._external_brain_names[0]: value}
|
|
elif self._num_external_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:
|
|
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 = self._n_agents[b]
|
|
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] = [""] * n_agent
|
|
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])))
|
|
|
|
outputs = self.communicator.exchange(
|
|
self._generate_step_input(vector_action, memory, text_action, value)
|
|
)
|
|
if outputs is None:
|
|
raise KeyboardInterrupt
|
|
rl_output = outputs.rl_output
|
|
s = self._get_state(rl_output)
|
|
self._global_done = s[1]
|
|
for _b in self._external_brain_names:
|
|
self._n_agents[_b] = len(s[0][_b].agents)
|
|
return s[0]
|
|
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._close()
|
|
else:
|
|
raise UnityEnvironmentException("No Unity environment is loaded.")
|
|
|
|
def _close(self):
|
|
self._loaded = False
|
|
self.communicator.close()
|
|
if self.proc1 is not None:
|
|
self.proc1.kill()
|
|
|
|
@staticmethod
|
|
def _flatten(arr):
|
|
"""
|
|
Converts arrays to list.
|
|
: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
|
|
|
|
@staticmethod
|
|
def _process_pixels(image_bytes, gray_scale):
|
|
"""
|
|
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 gray_scale:
|
|
s = np.mean(s, axis=2)
|
|
s = np.reshape(s, [s.shape[0], s.shape[1], 1])
|
|
return s
|
|
|
|
def _get_state(self, output: UnityRLOutput) -> (AllBrainInfo, bool):
|
|
"""
|
|
Collects experience information from all external brains in environment at current step.
|
|
:return: a dictionary of BrainInfo objects.
|
|
"""
|
|
_data = {}
|
|
global_done = output.global_done
|
|
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, value) -> 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],
|
|
)
|
|
if b in value:
|
|
action.value = value[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
|