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119 行
4.3 KiB
119 行
4.3 KiB
import os
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import json
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import math
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from .exception import CurriculumError
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import logging
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logger = logging.getLogger("mlagents.trainers")
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class Curriculum(object):
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def __init__(self, location, default_reset_parameters):
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"""
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Initializes a Curriculum object.
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:param location: Path to JSON defining curriculum.
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:param default_reset_parameters: Set of reset parameters for
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environment.
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"""
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self.max_lesson_num = 0
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self.measure = None
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self._lesson_num = 0
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# The name of the brain should be the basename of the file without the
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# extension.
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self._brain_name = os.path.basename(location).split(".")[0]
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try:
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with open(location) as data_file:
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self.data = json.load(data_file)
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except IOError:
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raise CurriculumError("The file {0} could not be found.".format(location))
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except UnicodeDecodeError:
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raise CurriculumError("There was an error decoding {}".format(location))
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self.smoothing_value = 0
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for key in [
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"parameters",
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"measure",
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"thresholds",
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"min_lesson_length",
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"signal_smoothing",
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]:
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if key not in self.data:
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raise CurriculumError(
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"{0} does not contain a " "{1} field.".format(location, key)
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)
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self.smoothing_value = 0
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self.measure = self.data["measure"]
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self.min_lesson_length = self.data["min_lesson_length"]
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self.max_lesson_num = len(self.data["thresholds"])
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parameters = self.data["parameters"]
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for key in parameters:
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if key not in default_reset_parameters:
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raise CurriculumError(
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"The parameter {0} in Curriculum {1} is not present in "
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"the Environment".format(key, location)
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)
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if len(parameters[key]) != self.max_lesson_num + 1:
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raise CurriculumError(
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"The parameter {0} in Curriculum {1} must have {2} values "
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"but {3} were found".format(
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key, location, self.max_lesson_num + 1, len(parameters[key])
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)
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)
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@property
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def lesson_num(self):
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return self._lesson_num
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@lesson_num.setter
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def lesson_num(self, lesson_num):
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self._lesson_num = max(0, min(lesson_num, self.max_lesson_num))
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def increment_lesson(self, measure_val):
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"""
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Increments the lesson number depending on the progress given.
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:param measure_val: Measure of progress (either reward or percentage
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steps completed).
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:return Whether the lesson was incremented.
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"""
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if not self.data or not measure_val or math.isnan(measure_val):
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return False
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if self.data["signal_smoothing"]:
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measure_val = self.smoothing_value * 0.25 + 0.75 * measure_val
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self.smoothing_value = measure_val
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if self.lesson_num < self.max_lesson_num:
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if measure_val > self.data["thresholds"][self.lesson_num]:
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self.lesson_num += 1
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config = {}
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parameters = self.data["parameters"]
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for key in parameters:
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config[key] = parameters[key][self.lesson_num]
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logger.info(
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"{0} lesson changed. Now in lesson {1}: {2}".format(
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self._brain_name,
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self.lesson_num,
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", ".join([str(x) + " -> " + str(config[x]) for x in config]),
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)
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)
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return True
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return False
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def get_config(self, lesson=None):
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"""
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Returns reset parameters which correspond to the lesson.
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:param lesson: The lesson you want to get the config of. If None, the
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current lesson is returned.
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:return: The configuration of the reset parameters.
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"""
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if not self.data:
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return {}
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if lesson is None:
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lesson = self.lesson_num
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lesson = max(0, min(lesson, self.max_lesson_num))
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config = {}
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parameters = self.data["parameters"]
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for key in parameters:
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config[key] = parameters[key][lesson]
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return config
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