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 .curriculum import Curriculum 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, curriculum=None, 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 'editor', a new environment will not be launched # and the communicator will directly try to connect to an existing unity environment. 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, "vectorObservationSpaceType": brain_param.vector_observation_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 self._curriculum = Curriculum(curriculum, self._resetParameters) 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 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 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): _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 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: 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) -> 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 = 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) ) 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) -> 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