ml-agents/ml-agents/mlagents/trainers/meta_curriculum.py

"""Contains the MetaCurriculum class."""

import os
from typing import Any, Dict, Set
from mlagents.trainers.curriculum import Curriculum
from mlagents.trainers.exception import MetaCurriculumError

import logging

logger = logging.getLogger("mlagents.trainers")


class MetaCurriculum(object):
    """A MetaCurriculum holds curriculums. Each curriculum is associated to a
    particular brain in the environment.
    """

    def __init__(
        self, curriculum_folder: str, default_reset_parameters: Dict[str, Any]
    ):
        """Initializes a MetaCurriculum object.

        Args:
            curriculum_folder (str): The relative or absolute path of the
                folder which holds the curriculums for this environment.
                The folder should contain JSON files whose names are the
                brains that the curriculums belong to.
            default_reset_parameters (dict): The default reset parameters
                of the environment.
        """
        used_reset_parameters: Set[str] = set()
        self._brains_to_curriculums: Dict[str, Curriculum] = {}

        try:
            for curriculum_filename in os.listdir(curriculum_folder):
                # This process requires JSON files
                if not curriculum_filename.lower().endswith(".json"):
                    continue
                brain_name = curriculum_filename.split(".")[0]
                curriculum_filepath = os.path.join(
                    curriculum_folder, curriculum_filename
                )
                curriculum = Curriculum(curriculum_filepath, default_reset_parameters)
                config_keys: Set[str] = set(curriculum.get_config().keys())

                # Check if any two curriculums use the same reset params.
                if config_keys & used_reset_parameters:
                    logger.warning(
                        "Two or more curriculums will "
                        "attempt to change the same reset "
                        "parameter. The result will be "
                        "non-deterministic."
                    )

                used_reset_parameters.update(config_keys)
                self._brains_to_curriculums[brain_name] = curriculum
        except NotADirectoryError:
            raise MetaCurriculumError(
                curriculum_folder + " is not a "
                "directory. Refer to the ML-Agents "
                "curriculum learning docs."
            )

    @property
    def brains_to_curriculums(self):
        """A dict from brain_name to the brain's curriculum."""
        return self._brains_to_curriculums

    @property
    def lesson_nums(self):
        """A dict from brain name to the brain's curriculum's lesson number."""
        lesson_nums = {}
        for brain_name, curriculum in self.brains_to_curriculums.items():
            lesson_nums[brain_name] = curriculum.lesson_num

        return lesson_nums

    @lesson_nums.setter
    def lesson_nums(self, lesson_nums):
        for brain_name, lesson in lesson_nums.items():
            self.brains_to_curriculums[brain_name].lesson_num = lesson

    def _lesson_ready_to_increment(self, brain_name, reward_buff_size):
        """Determines whether the curriculum of a specified brain is ready
        to attempt an increment.

        Args:
            brain_name (str): The name of the brain whose curriculum will be
                checked for readiness.
            reward_buff_size (int): The size of the reward buffer of the trainer
                that corresponds to the specified brain.

        Returns:
            Whether the curriculum of the specified brain should attempt to
            increment its lesson.
        """
        return reward_buff_size >= (
            self.brains_to_curriculums[brain_name].min_lesson_length
        )

    def increment_lessons(self, measure_vals, reward_buff_sizes=None):
        """Attempts to increments all the lessons of all the curriculums in this
        MetaCurriculum. Note that calling this method does not guarantee the
        lesson of a curriculum will increment. The lesson of a curriculum will
        only increment if the specified measure threshold defined in the
        curriculum has been reached and the minimum number of episodes in the
        lesson have been completed.

        Args:
            measure_vals (dict): A dict of brain name to measure value.
            reward_buff_sizes (dict): A dict of brain names to the size of their
                corresponding reward buffers.

        Returns:
            A dict from brain name to whether that brain's lesson number was
            incremented.
        """
        ret = {}
        if reward_buff_sizes:
            for brain_name, buff_size in reward_buff_sizes.items():
                if self._lesson_ready_to_increment(brain_name, buff_size):
                    measure_val = measure_vals[brain_name]
                    ret[brain_name] = self.brains_to_curriculums[
                        brain_name
                    ].increment_lesson(measure_val)
        else:
            for brain_name, measure_val in measure_vals.items():
                ret[brain_name] = self.brains_to_curriculums[
                    brain_name
                ].increment_lesson(measure_val)
        return ret

    def set_all_curriculums_to_lesson_num(self, lesson_num):
        """Sets all the curriculums in this meta curriculum to a specified
        lesson number.

        Args:
            lesson_num (int): The lesson number which all the curriculums will
                be set to.
        """
        for _, curriculum in self.brains_to_curriculums.items():
            curriculum.lesson_num = lesson_num

    def get_config(self):
        """Get the combined configuration of all curriculums in this
        MetaCurriculum.

        Returns:
            A dict from parameter to value.
        """
        config = {}

        for _, curriculum in self.brains_to_curriculums.items():
            curr_config = curriculum.get_config()
            config.update(curr_config)

        return config