Unity 机器学习代理工具包 (ML-Agents) 是一个开源项目,它使游戏和模拟能够作为训练智能代理的环境。
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# # Unity ML Agents
import logging
import tensorflow as tf
from unityagents import UnityException
logger = logging.getLogger("unityagents")
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):
"""
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.brain = env.brains[self.brain_name]
self.trainer_parameters = trainer_parameters
self.is_training = training
self.sess = sess
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 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(self):
"""
Increment the step count of the trainer
"""
raise UnityTrainerException("The increment_step method was not implemented.")
def update_last_reward(self):
"""
Updates the last reward
"""
raise UnityTrainerException("The update_last_reward method was not implemented.")
def take_action(self, info):
"""
Decides actions given state/observation information, and takes them in environment.
:param info: Current BrainInfo from environment.
:return: a tupple 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, info, next_info, take_action_outputs):
"""
Adds experiences to each agent's experience history.
:param info: Current BrainInfo.
:param next_info: Next BrainInfo.
:param take_action_outputs: The outputs of the take action method.
"""
raise UnityTrainerException("The add_experiences method was not implemented.")
def process_experiences(self, info):
"""
Checks agent histories for processing condition, and processes them as necessary.
Processing involves calculating value and advantage targets for model updating step.
:param info: Current 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, lesson_number):
"""
Saves training statistics to Tensorboard.
:param lesson_number: The lesson the trainer is at.
"""
raise UnityTrainerException("The write_summary method was not implemented.")
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