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