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354 行
17 KiB
354 行
17 KiB
# # Unity ML-Agents Toolkit
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# ## ML-Agent Learning
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"""Launches trainers for each External Brains in a Unity Environment."""
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import os
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import logging
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import yaml
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import re
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import numpy as np
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import tensorflow as tf
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from tensorflow.python.tools import freeze_graph
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from mlagents.envs.environment import UnityEnvironment
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from mlagents.envs.exception import UnityEnvironmentException
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from mlagents.trainers.ppo.trainer import PPOTrainer
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from mlagents.trainers.bc.offline_trainer import OfflineBCTrainer
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from mlagents.trainers.bc.online_trainer import OnlineBCTrainer
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from mlagents.trainers.meta_curriculum import MetaCurriculum
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from mlagents.trainers.exception import MetaCurriculumError
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class TrainerController(object):
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def __init__(self, env_path, run_id, save_freq, curriculum_folder,
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fast_simulation, load, train, worker_id, keep_checkpoints,
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lesson, seed, docker_target_name,
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trainer_config_path, no_graphics):
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"""
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:param env_path: Location to the environment executable to be loaded.
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:param run_id: The sub-directory name for model and summary statistics
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:param save_freq: Frequency at which to save model
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:param curriculum_folder: Folder containing JSON curriculums for the
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environment.
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:param fast_simulation: Whether to run the game at training speed.
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:param load: Whether to load the model or randomly initialize.
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:param train: Whether to train model, or only run inference.
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:param worker_id: Number to add to communication port (5005).
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Used for multi-environment
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:param keep_checkpoints: How many model checkpoints to keep.
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:param lesson: Start learning from this lesson.
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:param seed: Random seed used for training.
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:param docker_target_name: Name of docker volume that will contain all
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data.
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:param trainer_config_path: Fully qualified path to location of trainer
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configuration file.
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:param no_graphics: Whether to run the Unity simulator in no-graphics
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mode.
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"""
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if env_path is not None:
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# Strip out executable extensions if passed
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env_path = (env_path.strip()
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.replace('.app', '')
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.replace('.exe', '')
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.replace('.x86_64', '')
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.replace('.x86', ''))
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# Recognize and use docker volume if one is passed as an argument
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if not docker_target_name:
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self.docker_training = False
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self.trainer_config_path = trainer_config_path
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self.model_path = './models/{run_id}'.format(run_id=run_id)
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self.curriculum_folder = curriculum_folder
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self.summaries_dir = './summaries'
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else:
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self.docker_training = True
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self.trainer_config_path = \
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'/{docker_target_name}/{trainer_config_path}'.format(
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docker_target_name=docker_target_name,
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trainer_config_path = trainer_config_path)
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self.model_path = '/{docker_target_name}/models/{run_id}'.format(
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docker_target_name=docker_target_name,
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run_id=run_id)
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if env_path is not None:
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env_path = '/{docker_target_name}/{env_name}'.format(
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docker_target_name=docker_target_name, env_name=env_path)
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if curriculum_folder is not None:
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self.curriculum_folder = \
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'/{docker_target_name}/{curriculum_folder}'.format(
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docker_target_name=docker_target_name,
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curriculum_folder=curriculum_folder)
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self.summaries_dir = '/{docker_target_name}/summaries'.format(
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docker_target_name=docker_target_name)
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self.logger = logging.getLogger('mlagents.envs')
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self.run_id = run_id
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self.save_freq = save_freq
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self.lesson = lesson
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self.fast_simulation = fast_simulation
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self.load_model = load
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self.train_model = train
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self.worker_id = worker_id
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self.keep_checkpoints = keep_checkpoints
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self.trainers = {}
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self.seed = seed
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np.random.seed(self.seed)
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tf.set_random_seed(self.seed)
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self.env = UnityEnvironment(file_name=env_path,
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worker_id=self.worker_id,
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seed=self.seed,
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docker_training=self.docker_training,
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no_graphics=no_graphics)
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if env_path is None:
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self.env_name = 'editor_' + self.env.academy_name
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else:
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# Extract out name of environment
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self.env_name = os.path.basename(os.path.normpath(env_path))
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if curriculum_folder is None:
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self.meta_curriculum = None
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else:
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self.meta_curriculum = MetaCurriculum(self.curriculum_folder,
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self.env._resetParameters)
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if self.meta_curriculum:
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for brain_name in self.meta_curriculum.brains_to_curriculums.keys():
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if brain_name not in self.env.external_brain_names:
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raise MetaCurriculumError('One of the curriculums '
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'defined in ' +
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self.curriculum_folder + ' '
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'does not have a corresponding '
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'Brain. Check that the '
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'curriculum file has the same '
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'name as the Brain '
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'whose curriculum it defines.')
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def _get_measure_vals(self):
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if self.meta_curriculum:
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brain_names_to_measure_vals = {}
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for brain_name, curriculum \
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in self.meta_curriculum.brains_to_curriculums.items():
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if curriculum.measure == 'progress':
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measure_val = (self.trainers[brain_name].get_step /
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self.trainers[brain_name].get_max_steps)
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brain_names_to_measure_vals[brain_name] = measure_val
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elif curriculum.measure == 'reward':
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measure_val = np.mean(self.trainers[brain_name]
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.reward_buffer)
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brain_names_to_measure_vals[brain_name] = measure_val
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return brain_names_to_measure_vals
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else:
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return None
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def _save_model(self,steps=0):
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"""
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Saves current model to checkpoint folder.
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:param steps: Current number of steps in training process.
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:param saver: Tensorflow saver for session.
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"""
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for brain_name in self.trainers.keys():
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self.trainers[brain_name].save_model()
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self.logger.info('Saved Model')
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def _export_graph(self):
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"""
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Exports latest saved models to .bytes format for Unity embedding.
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"""
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for brain_name in self.trainers.keys():
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self.trainers[brain_name].export_model()
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def _initialize_trainers(self, trainer_config):
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"""
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Initialization of the trainers
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:param trainer_config: The configurations of the trainers
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"""
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trainer_parameters_dict = {}
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for brain_name in self.env.external_brain_names:
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trainer_parameters = trainer_config['default'].copy()
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trainer_parameters['summary_path'] = '{basedir}/{name}'.format(
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basedir=self.summaries_dir,
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name=str(self.run_id) + '_' + brain_name)
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trainer_parameters['model_path'] = '{basedir}/{name}'.format(
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basedir=self.model_path,
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name=brain_name)
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trainer_parameters['keep_checkpoints'] = self.keep_checkpoints
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if brain_name in trainer_config:
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_brain_key = brain_name
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while not isinstance(trainer_config[_brain_key], dict):
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_brain_key = trainer_config[_brain_key]
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for k in trainer_config[_brain_key]:
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trainer_parameters[k] = trainer_config[_brain_key][k]
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trainer_parameters_dict[brain_name] = trainer_parameters.copy()
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for brain_name in self.env.external_brain_names:
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if trainer_parameters_dict[brain_name]['trainer'] == 'offline_bc':
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self.trainers[brain_name] = OfflineBCTrainer(
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self.env.brains[brain_name],
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trainer_parameters_dict[brain_name], self.train_model,
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self.load_model, self.seed, self.run_id)
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elif trainer_parameters_dict[brain_name]['trainer'] == 'online_bc':
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self.trainers[brain_name] = OnlineBCTrainer(
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self.env.brains[brain_name],
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trainer_parameters_dict[brain_name], self.train_model,
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self.load_model, self.seed, self.run_id)
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elif trainer_parameters_dict[brain_name]['trainer'] == 'ppo':
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self.trainers[brain_name] = PPOTrainer(
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self.env.brains[brain_name],
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self.meta_curriculum
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.brains_to_curriculums[brain_name]
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.min_lesson_length if self.meta_curriculum else 0,
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trainer_parameters_dict[brain_name],
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self.train_model, self.load_model, self.seed, self.run_id)
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else:
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raise UnityEnvironmentException('The trainer config contains '
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'an unknown trainer type for '
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'brain {}'
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.format(brain_name))
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def _load_config(self):
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try:
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with open(self.trainer_config_path) as data_file:
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trainer_config = yaml.load(data_file)
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return trainer_config
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except IOError:
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raise UnityEnvironmentException('Parameter file could not be found '
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'at {}.'
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.format(self.trainer_config_path))
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except UnicodeDecodeError:
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raise UnityEnvironmentException('There was an error decoding '
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'Trainer Config from this path : {}'
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.format(self.trainer_config_path))
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@staticmethod
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def _create_model_path(model_path):
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try:
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if not os.path.exists(model_path):
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os.makedirs(model_path)
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except Exception:
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raise UnityEnvironmentException('The folder {} containing the '
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'generated model could not be '
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'accessed. Please make sure the '
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'permissions are set correctly.'
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.format(model_path))
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def _reset_env(self):
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"""Resets the environment.
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Returns:
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A Data structure corresponding to the initial reset state of the
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environment.
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"""
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if self.meta_curriculum is not None:
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return self.env.reset(config=self.meta_curriculum.get_config(),
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train_mode=self.fast_simulation)
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else:
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return self.env.reset(train_mode=self.fast_simulation)
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def start_learning(self):
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# TODO: Should be able to start learning at different lesson numbers
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# for each curriculum.
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if self.meta_curriculum is not None:
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self.meta_curriculum.set_all_curriculums_to_lesson_num(self.lesson)
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trainer_config = self._load_config()
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self._create_model_path(self.model_path)
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tf.reset_default_graph()
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# Prevent a single session from taking all GPU memory.
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self._initialize_trainers(trainer_config)
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for _, t in self.trainers.items():
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self.logger.info(t)
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global_step = 0 # This is only for saving the model
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curr_info = self._reset_env()
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if self.train_model:
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for brain_name, trainer in self.trainers.items():
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trainer.write_tensorboard_text('Hyperparameters',
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trainer.parameters)
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try:
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while any([t.get_step <= t.get_max_steps \
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for k, t in self.trainers.items()]) \
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or not self.train_model:
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if self.meta_curriculum:
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# Get the sizes of the reward buffers.
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reward_buff_sizes = {k:len(t.reward_buffer) \
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for (k,t) in self.trainers.items()}
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# Attempt to increment the lessons of the brains who
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# were ready.
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lessons_incremented = \
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self.meta_curriculum.increment_lessons(
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self._get_measure_vals(),
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reward_buff_sizes=reward_buff_sizes)
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# If any lessons were incremented or the environment is
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# ready to be reset
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if (self.meta_curriculum
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and any(lessons_incremented.values())):
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curr_info = self._reset_env()
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for brain_name, trainer in self.trainers.items():
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trainer.end_episode()
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for brain_name, changed in lessons_incremented.items():
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if changed:
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self.trainers[brain_name].reward_buffer.clear()
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elif self.env.global_done:
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curr_info = self._reset_env()
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for brain_name, trainer in self.trainers.items():
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trainer.end_episode()
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# Decide and take an action
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take_action_vector, \
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take_action_memories, \
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take_action_text, \
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take_action_value, \
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take_action_outputs \
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= {}, {}, {}, {}, {}
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for brain_name, trainer in self.trainers.items():
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(take_action_vector[brain_name],
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take_action_memories[brain_name],
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take_action_text[brain_name],
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take_action_value[brain_name],
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take_action_outputs[brain_name]) = \
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trainer.take_action(curr_info)
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new_info = self.env.step(vector_action=take_action_vector,
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memory=take_action_memories,
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text_action=take_action_text,
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value=take_action_value)
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for brain_name, trainer in self.trainers.items():
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trainer.add_experiences(curr_info, new_info,
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take_action_outputs[brain_name])
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trainer.process_experiences(curr_info, new_info)
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if trainer.is_ready_update() and self.train_model \
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and trainer.get_step <= trainer.get_max_steps:
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# Perform gradient descent with experience buffer
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trainer.update_policy()
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# Write training statistics to Tensorboard.
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if self.meta_curriculum is not None:
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trainer.write_summary(
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global_step,
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lesson_num=self.meta_curriculum
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.brains_to_curriculums[brain_name]
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.lesson_num)
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else:
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trainer.write_summary(global_step)
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if self.train_model \
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and trainer.get_step <= trainer.get_max_steps:
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trainer.increment_step_and_update_last_reward()
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global_step += 1
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if global_step % self.save_freq == 0 and global_step != 0 \
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and self.train_model:
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# Save Tensorflow model
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self._save_model(steps=global_step)
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curr_info = new_info
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# Final save Tensorflow model
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if global_step != 0 and self.train_model:
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self._save_model(steps=global_step)
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except KeyboardInterrupt:
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print('--------------------------Now saving model--------------'
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'-----------')
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if self.train_model:
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self.logger.info('Learning was interrupted. Please wait '
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'while the graph is generated.')
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self._save_model(steps=global_step)
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pass
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self.env.close()
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if self.train_model:
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self._export_graph()
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