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class UnityEnvironment(object): |
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def __init__(self, file_name, worker_id=0, |
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base_port=5005, curriculum = None): |
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base_port=5005, curriculum=None): |
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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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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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.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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launch_string = None |
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if platform == "linux" or platform == "linux2": |
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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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"Provided filename does not match any environments." |
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.format(true_filename)) |
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else: |
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# Launch Unity environment |
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proc1 = subprocess.Popen( |
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self._socket.settimeout(30) |
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self._socket.settimeout(30) |
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try: |
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try: |
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self._socket.listen(1) |
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p = json.loads(p) |
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except socket.timeout as e: |
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raise UnityEnvironmentException( |
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"The Unity environment took too long to respond. Make sure {} does not need user interaction to launch " |
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"and that the Academy and the external Brain(s) are attached to objects in the Scene.".format( |
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str(file_name))) |
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"The Unity environment took too long to respond. Make sure {} does not need user interaction to " |
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"launch and that the Academy and the external Brain(s) are attached to objects in the Scene." |
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.format(str(file_name))) |
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if "apiNumber" not in p: |
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self._unity_api = "API-1" |
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else: |
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"The API number is not compatible between Unity and python. Python API : {0}, Unity API : " |
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"{1}.".format(self._python_api, self._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._python_api, self._unity_api)) |
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self._data = {} |
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self._global_done = None |
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logger.info("\n'{}' started successfully!".format(self._academy_name)) |
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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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"You will not be able to pass actions to your agent(s).") |
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except UnityEnvironmentException: |
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proc1.kill() |
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self.close() |
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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 _recv_bytes(self): |
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s = self._conn.recv(self._buffer_size) |
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message_length = struct.unpack("I", bytearray(s[:4]))[0] |
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config = self._curriculum.get_lesson(progress) if config is None else config |
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if old_lesson != self._curriculum.get_lesson_number(): |
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logger.info("\nLesson changed. Now in Lesson {0} : \t{1}" |
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.format(self._curriculum.get_lesson_number(), |
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', '.join([str(x)+' -> '+str(config[x]) for x in config]))) |
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.format(self._curriculum.get_lesson_number(), |
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', '.join([str(x) + ' -> ' + str(config[x]) for x in config]))) |
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.format(self._curriculum.get_lesson_number(), |
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', '.join([str(x)+' -> '+str(config[x]) for x in config]))) |
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.format(self._curriculum.get_lesson_number(), |
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', '.join([str(x) + ' -> ' + str(config[x]) for x in config]))) |
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if self._loaded: |
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self._conn.send(b"RESET") |
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self._conn.recv(self._buffer_size) |
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raise UnityActionException("Brain {0} has an invalid state. " |
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"Expecting {1} {2} state but received {3}." |
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.format(b, n_agent if self._brains[b].state_space_type == "discrete" |
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else str(self._brains[b].state_space_size * n_agent), |
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self._brains[b].state_space_type, |
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len(state_dict["states"]))) |
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else str(self._brains[b].state_space_size * n_agent), |
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self._brains[b].state_space_type, |
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len(state_dict["states"]))) |
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if n_agent > 0 : |
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actions = np.array(state_dict["actions"]).reshape((n_agent, -1)) |
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else : |
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if n_agent > 0: |
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actions = np.array(state_dict["actions"]).reshape((n_agent, -1)) |
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else: |
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actions = np.array([]) |
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observations = [] |
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arr = [float(x) for x in arr] |
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return arr |
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def step(self, action = None, memory=None, value=None): |
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def step(self, action=None, memory=None, value=None): |
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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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"and 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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"There are no external brains in the environment, " |
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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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"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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"There are no external brains in the environment, " |
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elif self._num_external_brains > 1: |
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elif self._num_external_brains > 1: |
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"There are no external brains in the environment, " |
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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(action.keys()) + list(memory.keys()) + list(value.keys()): |
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"in the environment". format(brain_name)) |
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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 = len(self._data[b].agents) |
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raise UnityActionException( |
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"There was a mismatch between the provided memory and environment's expectation: " |
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"The brain {0} expected {1} memories but was given {2}" |
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.format(b, self._brains[b].memory_space_size * n_agent, len(memory[b]))) |
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.format(b, self._brains[b].memory_space_size * n_agent, len(memory[b]))) |
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if not ((self._brains[b].action_space_type == "discrete" and len(action[b]) == n_agent) or |
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(self._brains[b].action_space_type == "continuous" and len( |
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action[b]) == self._brains[b].action_space_size * n_agent)): |
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.format(b, n_agent if self._brains[b].action_space_type == "discrete" else |
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.format(b, n_agent if self._brains[b].action_space_type == "discrete" else |
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str(action[b]))) |
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str(action[b]))) |
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self._conn.send(b"STEP") |
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self._send_action(action, memory, value) |
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return self._get_state() |
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self._socket.close() |
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self._loaded = False |
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else: |
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raise UnityEnvironmentException("No Unity environment is loaded.") |
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raise UnityEnvironmentException("No Unity environment is loaded.") |
Arthur Juliani
7 年前