# # Unity ML-Agents Toolkit import logging import tensorflow as tf import numpy as np from unityagents import UnityException, AllBrainInfo logger = logging.getLogger("unitytrainers") class UnityTrainerException(UnityException): """ Related to errors with the Trainer. """ pass class Trainer(object): """This class is the abstract class for the unitytrainers""" def __init__(self, sess, env, brain_name, trainer_parameters, training, run_id): """ Responsible for collecting experiences and training a neural network model. :param sess: Tensorflow session. :param env: The UnityEnvironment. :param trainer_parameters: The parameters for the trainer (dictionary). :param training: Whether the trainer is set for training. """ self.brain_name = brain_name self.run_id = run_id self.brain = env.brains[self.brain_name] self.trainer_parameters = trainer_parameters self.is_training = training self.sess = sess self.stats = {} self.summary_writer = None def __str__(self): return '''Empty Trainer''' @property def parameters(self): """ Returns the trainer parameters of the trainer. """ raise UnityTrainerException("The parameters property was not implemented.") @property def graph_scope(self): """ Returns the graph scope of the trainer. """ raise UnityTrainerException("The graph_scope property was not implemented.") @property def get_max_steps(self): """ Returns the maximum number of steps. Is used to know when the trainer should be stopped. :return: The maximum number of steps of the trainer """ raise UnityTrainerException("The get_max_steps property was not implemented.") @property def get_step(self): """ Returns the number of training steps the trainer has performed :return: the step count of the trainer """ raise UnityTrainerException("The get_step property was not implemented.") @property def get_last_reward(self): """ Returns the last reward the trainer has had :return: the new last reward """ raise UnityTrainerException("The get_last_reward property was not implemented.") def increment_step_and_update_last_reward(self): """ Increment the step count of the trainer and updates the last reward """ raise UnityTrainerException("The increment_step_and_update_last_reward method was not implemented.") def take_action(self, all_brain_info: AllBrainInfo): """ Decides actions given state/observation information, and takes them in environment. :param all_brain_info: A dictionary of brain names and BrainInfo from environment. :return: a tuple containing action, memories, values and an object to be passed to add experiences """ raise UnityTrainerException("The take_action method was not implemented.") def add_experiences(self, curr_info: AllBrainInfo, next_info: AllBrainInfo, take_action_outputs): """ Adds experiences to each agent's experience history. :param curr_info: Current AllBrainInfo. :param next_info: Next AllBrainInfo. :param take_action_outputs: The outputs of the take action method. """ raise UnityTrainerException("The add_experiences method was not implemented.") def process_experiences(self, current_info: AllBrainInfo, next_info: AllBrainInfo): """ Checks agent histories for processing condition, and processes them as necessary. Processing involves calculating value and advantage targets for model updating step. :param current_info: Dictionary of all current-step brains and corresponding BrainInfo. :param next_info: Dictionary of all next-step brains and corresponding BrainInfo. """ raise UnityTrainerException("The process_experiences method was not implemented.") def end_episode(self): """ A signal that the Episode has ended. The buffer must be reset. Get only called when the academy resets. """ raise UnityTrainerException("The end_episode method was not implemented.") def is_ready_update(self): """ Returns whether or not the trainer has enough elements to run update model :return: A boolean corresponding to wether or not update_model() can be run """ raise UnityTrainerException("The is_ready_update method was not implemented.") def update_model(self): """ Uses training_buffer to update model. """ raise UnityTrainerException("The update_model method was not implemented.") def write_summary(self, global_step, lesson_num=0): """ Saves training statistics to Tensorboard. :param global_step: The number of steps the simulation has been going for :param lesson_number: The lesson the trainer is at. """ if global_step % self.trainer_parameters['summary_freq'] == 0 and global_step != 0: is_training = "Training." if self.is_training and self.get_step <= self.get_max_steps else "Not Training." if len(self.stats['cumulative_reward']) > 0: mean_reward = np.mean(self.stats['cumulative_reward']) logger.info(" {}: {}: Step: {}. Mean Reward: {:0.3f}. Std of Reward: {:0.3f}. {}" .format(self.run_id, self.brain_name, min(self.get_step, self.get_max_steps), mean_reward, np.std(self.stats['cumulative_reward']), is_training)) else: logger.info(" {}: {}: Step: {}. No episode was completed since last summary. {}" .format(self.run_id, self.brain_name, self.get_step, is_training)) summary = tf.Summary() for key in self.stats: if len(self.stats[key]) > 0: stat_mean = float(np.mean(self.stats[key])) summary.value.add(tag='Info/{}'.format(key), simple_value=stat_mean) self.stats[key] = [] summary.value.add(tag='Info/Lesson', simple_value=lesson_num) self.summary_writer.add_summary(summary, self.get_step) self.summary_writer.flush() def write_tensorboard_text(self, key, input_dict): """ Saves text to Tensorboard. Note: Only works on tensorflow r1.2 or above. :param key: The name of the text. :param input_dict: A dictionary that will be displayed in a table on Tensorboard. """ try: s_op = tf.summary.text(key, tf.convert_to_tensor(([[str(x), str(input_dict[x])] for x in input_dict]))) s = self.sess.run(s_op) self.summary_writer.add_summary(s, self.get_step) except: logger.info("Cannot write text summary for Tensorboard. Tensorflow version must be r1.2 or above.") pass