Source code for ribs.emitters._evolution_strategy_emitter

"""Provides the EvolutionStrategyEmitter."""
import numpy as np

from ribs._utils import check_1d_shape, validate_batch_args
from ribs.emitters._emitter_base import EmitterBase
from ribs.emitters.opt import _get_es
from ribs.emitters.rankers import _get_ranker

[docs]class EvolutionStrategyEmitter(EmitterBase): """Adapts a distribution of solutions with an ES. This emitter originates in `Fontaine 2020 <>`_. The multivariate Gaussian solution distribution begins at ``x0`` with standard deviation ``sigma0``. Based on how the generated solutions are ranked (see ``ranker``), the ES then adapts the mean and covariance of the distribution. Args: archive (ribs.archives.ArchiveBase): An archive to use when creating and inserting solutions. For instance, this can be :class:`ribs.archives.GridArchive`. x0 (np.ndarray): Initial solution. Must be 1-dimensional. sigma0 (float): Initial step size / standard deviation. ranker (Callable or str): The ranker is a :class:`RankerBase` object that orders the solutions after they have been evaluated in the environment. This parameter may be a callable (e.g. a class or a lambda function) that takes in no parameters and returns an instance of :class:`RankerBase`, or it may be a full or abbreviated ranker name as described in :meth:`ribs.emitters.rankers.get_ranker`. es (Callable or str): The evolution strategy is an :class:`EvolutionStrategyBase` object that is used to adapt the distribution from which new solutions are sampled. This parameter may be a callable (e.g. a class or a lambda function) that takes in the parameters of :class:`EvolutionStrategyBase` along with kwargs provided by the ``es_kwargs`` argument, or it may be a full or abbreviated optimizer name as described in :mod:`ribs.emitters.opt`. es_kwargs (dict): Additional arguments to pass to the evolution strategy optimizer. See the evolution-strategy-based optimizers in :mod:`ribs.emitters.opt` for the arguments allowed by each optimizer. selection_rule ("mu" or "filter"): Method for selecting parents for the evolution strategy. With "mu" selection, the first half of the solutions will be selected as parents, while in "filter", any solutions that were added to the archive will be selected. restart_rule (int, "no_improvement", and "basic"): Method to use when checking for restarts. If given an integer, then the emitter will restart after this many iterations, where each iteration is a call to :meth:`tell`. With "basic", only the default CMA-ES convergence rules will be used, while with "no_improvement", the emitter will restart when none of the proposed solutions were added to the archive. bounds (None or array-like): Bounds of the solution space. As suggested in `Biedrzycki 2020 <>`_, solutions are resampled until they fall within these bounds. Pass None to indicate there are no bounds. Alternatively, pass an array-like to specify the bounds for each dim. Each element in this array-like can be None to indicate no bound, or a tuple of ``(lower_bound, upper_bound)``, where ``lower_bound`` or ``upper_bound`` may be None to indicate no bound. batch_size (int): Number of solutions to return in :meth:`ask`. If not passed in, a batch size will be automatically calculated using the default CMA-ES rules. seed (int): Value to seed the random number generator. Set to None to avoid a fixed seed. Raises: ValueError: There is an error in x0 or initial_solutions. ValueError: There is an error in the bounds configuration. ValueError: If ``restart_rule``, ``selection_rule``, or ``ranker`` is invalid. """ def __init__( self, archive, *, x0, sigma0, ranker="2imp", es="cma_es", es_kwargs=None, selection_rule="filter", restart_rule="no_improvement", bounds=None, batch_size=None, seed=None, ): self._rng = np.random.default_rng(seed) self._x0 = np.array(x0, dtype=archive.dtype) check_1d_shape(self._x0, "x0", archive.solution_dim, "archive.solution_dim") self._sigma0 = sigma0 EmitterBase.__init__( self, archive, solution_dim=archive.solution_dim, bounds=bounds, ) if selection_rule not in ["mu", "filter"]: raise ValueError(f"Invalid selection_rule {selection_rule}") self._selection_rule = selection_rule self._restart_rule = restart_rule self._restarts = 0 self._itrs = 0 # Check if the restart_rule is valid, discard check_restart result. _ = self._check_restart(0) opt_seed = None if seed is None else self._rng.integers(10_000) self._opt = _get_es(es, sigma0=sigma0, batch_size=batch_size, solution_dim=self._solution_dim, seed=opt_seed, dtype=self.archive.dtype, **(es_kwargs if es_kwargs is not None else {})) self._opt.reset(self._x0) self._ranker = _get_ranker(ranker) self._ranker.reset(self, archive, self._rng) self._batch_size = self._opt.batch_size @property def x0(self): """numpy.ndarray: Initial solution for the optimizer.""" return self._x0 @property def batch_size(self): """int: Number of solutions to return in :meth:`ask`.""" return self._batch_size @property def restarts(self): """int: The number of restarts for this emitter.""" return self._restarts @property def itrs(self): """int: The number of iterations for this emitter, where each iteration is a call to :meth:`tell`.""" return self._itrs
[docs] def ask(self): """Samples new solutions from a multivariate Gaussian. The multivariate Gaussian is parameterized by the evolution strategy optimizer ``self._opt``. Returns: (batch_size, :attr:`solution_dim`) array -- a batch of new solutions to evaluate. """ return self._opt.ask(self.lower_bounds, self.upper_bounds)
def _check_restart(self, num_parents): """Emitter-side checks for restarting the optimizer. The optimizer also has its own checks. Args: num_parents (int): The number of solution to propagate to the next generation from the solutions generated by CMA-ES. Raises: ValueError: If :attr:`restart_rule` is invalid. """ if isinstance(self._restart_rule, (int, np.integer)): return self._itrs % self._restart_rule == 0 if self._restart_rule == "no_improvement": return num_parents == 0 if self._restart_rule == "basic": return False raise ValueError(f"Invalid restart_rule {self._restart_rule}")
[docs] def tell(self, solution_batch, objective_batch, measures_batch, status_batch, value_batch, metadata_batch=None): """Gives the emitter results from evaluating solutions. The solutions are ranked based on the `rank()` function defined by `self._ranker`. Then, the ranked solutions are passed to CMA-ES for adaptation. This function also checks for restart condition and restarts CMA-ES when needed. Args: solution_batch (array-like): (batch_size, :attr:`solution_dim`) array of solutions generated by this emitter's :meth:`ask()` method. objective_batch (array-like): 1D array containing the objective function value of each solution. measures_batch (array-like): (batch_size, measure space dimension) array with the measure space coordinates of each solution. status_batch (array-like): 1D array of :class:`ribs.archive.AddStatus` returned by a series of calls to archive's :meth:`add()` method. value_batch (array-like): 1D array of floats returned by a series of calls to archive's :meth:`add()` method. For what these floats represent, refer to :meth:`ribs.archives.add()`. metadata_batch (array-like): 1D object array containing a metadata object for each solution. """ # Preprocessing arguments. solution_batch = np.asarray(solution_batch) objective_batch = np.asarray(objective_batch) measures_batch = np.asarray(measures_batch) status_batch = np.asarray(status_batch) value_batch = np.asarray(value_batch) batch_size = solution_batch.shape[0] metadata_batch = (np.empty(batch_size, dtype=object) if metadata_batch is None else np.asarray(metadata_batch, dtype=object)) # Validate arguments. validate_batch_args(archive=self.archive, solution_batch=solution_batch, objective_batch=objective_batch, measures_batch=measures_batch, status_batch=status_batch, value_batch=value_batch, metadata_batch=metadata_batch) # Increase iteration counter. self._itrs += 1 # Count number of new solutions. new_sols = status_batch.astype(bool).sum() # Sort the solutions using ranker. indices, ranking_values = self._ranker.rank( self, self.archive, self._rng, solution_batch, objective_batch, measures_batch, status_batch, value_batch, metadata_batch) # Select the number of parents. num_parents = (new_sols if self._selection_rule == "filter" else self._batch_size // 2) # Update Evolution Strategy. self._opt.tell(indices, num_parents) # Check for reset. if (self._opt.check_stop(ranking_values[indices]) or self._check_restart(new_sols)): new_x0 = self.archive.sample_elites(1).solution_batch[0] self._opt.reset(new_x0) self._ranker.reset(self, self.archive, self._rng) self._restarts += 1