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from typing import Dict, List, Tuple, Optional
from mlagents.trainers.settings import (
EnvironmentParameterSettings,
ParameterRandomizationSettings,
)
from collections import defaultdict
from mlagents.trainers.training_status import GlobalTrainingStatus, StatusType
from mlagents_envs.logging_util import get_logger
logger = get_logger(__name__)
class EnvironmentParameterManager:
def __init__(
self,
settings: Optional[Dict[str, EnvironmentParameterSettings]] = None,
run_seed: int = -1,
restore: bool = False,
):
"""
EnvironmentParameterManager manages all the environment parameters of a training
session. It determines when parameters should change and gives access to the
current sampler of each parameter.
:param settings: A dictionary from environment parameter to
EnvironmentParameterSettings.
:param run_seed: When the seed is not provided for an environment parameter,
this seed will be used instead.
:param restore: If true, the EnvironmentParameterManager will use the
GlobalTrainingStatus to try and reload the lesson status of each environment
parameter.
"""
if settings is None:
settings = {}
self._dict_settings = settings
for parameter_name in self._dict_settings.keys():
initial_lesson = GlobalTrainingStatus.get_parameter_state(
parameter_name, StatusType.LESSON_NUM
)
if initial_lesson is None or not restore:
GlobalTrainingStatus.set_parameter_state(
parameter_name, StatusType.LESSON_NUM, 0
)
self._smoothed_values: Dict[str, float] = defaultdict(float)
for key in self._dict_settings.keys():
self._smoothed_values[key] = 0.0
# Update the seeds of the samplers
self._set_sampler_seeds(run_seed)
def _set_sampler_seeds(self, seed):
"""
Sets the seeds for the samplers (if no seed was already present). Note that
using the provided seed.
"""
offset = 0
for settings in self._dict_settings.values():
for lesson in settings.curriculum:
if lesson.value.seed == -1:
lesson.value.seed = seed + offset
offset += 1
def get_minimum_reward_buffer_size(self, behavior_name: str) -> int:
"""
Calculates the minimum size of the reward buffer a behavior must use. This
method uses the 'min_lesson_length' sampler_parameter to determine this value.
:param behavior_name: The name of the behavior the minimum reward buffer
size corresponds to.
"""
result = 1
for settings in self._dict_settings.values():
for lesson in settings.curriculum:
if lesson.completion_criteria is not None:
if lesson.completion_criteria.behavior == behavior_name:
result = max(
result, lesson.completion_criteria.min_lesson_length
)
return result
def get_current_samplers(self) -> Dict[str, ParameterRandomizationSettings]:
"""
Creates a dictionary from environment parameter name to their corresponding
ParameterRandomizationSettings. If curriculum is used, the
ParameterRandomizationSettings corresponds to the sampler of the current lesson.
"""
samplers: Dict[str, ParameterRandomizationSettings] = {}
for param_name, settings in self._dict_settings.items():
lesson_num = GlobalTrainingStatus.get_parameter_state(
param_name, StatusType.LESSON_NUM
)
lesson = settings.curriculum[lesson_num]
samplers[param_name] = lesson.value
return samplers
def get_current_lesson_number(self) -> Dict[str, int]:
"""
Creates a dictionary from environment parameter to the current lesson number.
If not using curriculum, this number is always 0 for that environment parameter.
"""
result: Dict[str, int] = {}
for parameter_name in self._dict_settings.keys():
result[parameter_name] = GlobalTrainingStatus.get_parameter_state(
parameter_name, StatusType.LESSON_NUM
)
return result
def log_current_lesson(self, parameter_name: Optional[str] = None) -> None:
"""
Logs the current lesson number and sampler value of the parameter with name
parameter_name. If no parameter_name is provided, the values and lesson
numbers of all parameters will be displayed.
"""
if parameter_name is not None:
settings = self._dict_settings[parameter_name]
lesson_number = GlobalTrainingStatus.get_parameter_state(
parameter_name, StatusType.LESSON_NUM
)
lesson_name = settings.curriculum[lesson_number].name
lesson_value = settings.curriculum[lesson_number].value
logger.info(
f"Parameter '{parameter_name}' is in lesson '{lesson_name}' "
f"and has value '{lesson_value}'."
)
else:
for parameter_name, settings in self._dict_settings.items():
lesson_number = GlobalTrainingStatus.get_parameter_state(
parameter_name, StatusType.LESSON_NUM
)
lesson_name = settings.curriculum[lesson_number].name
lesson_value = settings.curriculum[lesson_number].value
logger.info(
f"Parameter '{parameter_name}' is in lesson '{lesson_name}' "
f"and has value '{lesson_value}'."
)
def update_lessons(
self,
trainer_steps: Dict[str, int],
trainer_max_steps: Dict[str, int],
trainer_reward_buffer: Dict[str, List[float]],
) -> Tuple[bool, bool]:
"""
Given progress metrics, calculates if at least one environment parameter is
in a new lesson and if at least one environment parameter requires the env
to reset.
:param trainer_steps: A dictionary from behavior_name to the number of training
steps this behavior's trainer has performed.
:param trainer_max_steps: A dictionary from behavior_name to the maximum number
of training steps this behavior's trainer has performed.
:param trainer_reward_buffer: A dictionary from behavior_name to the list of
the most recent episode returns for this behavior's trainer.
:returns: A tuple of two booleans : (True if any lesson has changed, True if
environment needs to reset)
"""
must_reset = False
updated = False
for param_name, settings in self._dict_settings.items():
lesson_num = GlobalTrainingStatus.get_parameter_state(
param_name, StatusType.LESSON_NUM
)
next_lesson_num = lesson_num + 1
lesson = settings.curriculum[lesson_num]
if (
lesson.completion_criteria is not None
and len(settings.curriculum) > next_lesson_num
):
behavior_to_consider = lesson.completion_criteria.behavior
if behavior_to_consider in trainer_steps:
(
must_increment,
new_smoothing,
) = lesson.completion_criteria.need_increment(
float(trainer_steps[behavior_to_consider])
/ float(trainer_max_steps[behavior_to_consider]),
trainer_reward_buffer[behavior_to_consider],
self._smoothed_values[param_name],
)
self._smoothed_values[param_name] = new_smoothing
if must_increment:
GlobalTrainingStatus.set_parameter_state(
param_name, StatusType.LESSON_NUM, next_lesson_num
)
self.log_current_lesson(param_name)
updated = True
if lesson.completion_criteria.require_reset:
must_reset = True
return updated, must_reset
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