def __init__(self,
archive,
x0,
sigma0,
selection_rule="filter",
restart_rule="no_improvement",
weight_rule="truncation",
bounds=None,
batch_size=None,
seed=None):
self._rng = np.random.default_rng(seed)
self._batch_size = batch_size
self._x0 = np.array(x0, dtype=archive.dtype)
self._sigma0 = sigma0
EmitterBase.__init__(
self,
archive,
len(self._x0),
bounds,
)
if selection_rule not in ["mu", "filter"]:
raise ValueError(f"Invalid selection_rule {selection_rule}")
self._selection_rule = selection_rule
if restart_rule not in ["basic", "no_improvement"]:
raise ValueError(f"Invalid restart_rule {restart_rule}")
self._restart_rule = restart_rule
opt_seed = None if seed is None else self._rng.integers(10_000)
self.opt = CMAEvolutionStrategy(sigma0, batch_size, self._solution_dim,
weight_rule, opt_seed,
self.archive.dtype)
self.opt.reset(self._x0)
self._num_parents = (self.opt.batch_size //
2 if selection_rule == "mu" else None)
self._target_behavior_dir = self._generate_random_direction()
self._batch_size = self.opt.batch_size
self._restarts = 0 # Currently not exposed publicly.
class OptimizingEmitter(EmitterBase):
"""Adapts a covariance matrix towards the objective.
This emitter originates in `Fontaine 2020
<https://arxiv.org/abs/1912.02400>`_. Initially, it starts at ``x0`` and
uses CMA-ES to optimize for objective values. After CMA-ES converges, the
emitter restarts the optimizer. It picks a random elite in the archive and
begins optimizing from there.
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.
sigma0 (float): Initial step size.
selection_rule ("mu" or "filter"): Method for selecting solutions in
CMA-ES. With "mu" selection, the first half of the solutions will be
selected, while in "filter", any solutions that were added to the
archive will be selected.
restart_rule ("no_improvement" or "basic"): Method to use when checking
for restart. 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.
weight_rule ("truncation" or "active"): Method for generating weights in
CMA-ES. Either "truncation" (positive weights only) or "active"
(include negative weights).
bounds (None or array-like): Bounds of the solution space. Solutions are
clipped to 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 automatically be calculated.
seed (int): Value to seed the random number generator. Set to None to
avoid a fixed seed.
Raises:
ValueError: If any of ``selection_rule``, ``restart_rule``, or
``weight_rule`` is invalid.
"""
def __init__(self,
archive,
x0,
sigma0,
selection_rule="mu",
restart_rule="basic",
weight_rule="truncation",
bounds=None,
batch_size=None,
seed=None):
self._rng = np.random.default_rng(seed)
self._batch_size = batch_size
self._x0 = np.array(x0, dtype=archive.dtype)
self._sigma0 = sigma0
EmitterBase.__init__(
self,
archive,
len(self._x0),
bounds,
)
if selection_rule not in ["mu", "filter"]:
raise ValueError(f"Invalid selection_rule {selection_rule}")
self._selection_rule = selection_rule
if restart_rule not in ["basic", "no_improvement"]:
raise ValueError(f"Invalid restart_rule {restart_rule}")
self._restart_rule = restart_rule
opt_seed = None if seed is None else self._rng.integers(10_000)
self.opt = CMAEvolutionStrategy(sigma0, batch_size, self._solution_dim,
weight_rule, opt_seed,
self.archive.dtype)
self.opt.reset(self._x0)
self._num_parents = (self.opt.batch_size //
2 if selection_rule == "mu" else None)
self._batch_size = self.opt.batch_size
self._restarts = 0 # Currently not exposed publicly.
@property
def x0(self):
"""numpy.ndarray: Initial solution for the optimizer."""
return self._x0
@property
def sigma0(self):
"""float: Initial step size for the CMA-ES optimizer."""
return self._sigma0
@property
def batch_size(self):
"""int: Number of solutions to return in :meth:`ask`."""
return self._batch_size
def ask(self):
"""Samples new solutions from a multivariate Gaussian.
The multivariate Gaussian is parameterized by the CMA-ES optimizer.
Returns:
``(batch_size, solution_dim)`` array -- contains ``batch_size`` 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.
"""
if self._restart_rule == "no_improvement":
return num_parents == 0
return False
def tell(self, solutions, objective_values, behavior_values, metadata=None):
"""Gives the emitter results from evaluating solutions.
As solutions are inserted into the archive, we record their objective
value as well as whether the solution was added to the archive. When
using "filter" selection, we rank the solutions first by whether they
were added, and second by the objective value, and when using "mu"
selection, we rank solely by objective. We then pass the ranked
solutions to the underlying CMA-ES optimizer to update the search
parameters.
Args:
solutions (numpy.ndarray): Array of solutions generated by this
emitter's :meth:`ask()` method.
objective_values (numpy.ndarray): 1D array containing the objective
function value of each solution.
behavior_values (numpy.ndarray): ``(n, <behavior space dimension>)``
array with the behavior space coordinates of each solution.
metadata (numpy.ndarray): 1D object array containing a metadata
object for each solution.
"""
# Tuples of (solution was added, objective value, index).
ranking_data = []
new_sols = 0
metadata = itertools.repeat(None) if metadata is None else metadata
for i, (sol, obj, beh, meta) in enumerate(
zip(solutions, objective_values, behavior_values, metadata)):
status, _ = self.archive.add(sol, obj, beh, meta)
added = bool(status)
ranking_data.append((added, obj, i))
if added:
new_sols += 1
if self._selection_rule == "filter":
# Sort by whether the solution was added into the archive, followed
# by objective value.
key = lambda x: (x[0], x[1])
elif self._selection_rule == "mu":
# Sort only by objective value.
key = lambda x: x[1]
ranking_data.sort(reverse=True, key=key)
indices = [d[2] for d in ranking_data]
num_parents = (new_sols if self._selection_rule == "filter" else
self._num_parents)
self.opt.tell(solutions[indices], num_parents)
# Check for reset.
if (self.opt.check_stop([obj for status, obj, i in ranking_data]) or
self._check_restart(new_sols)):
new_x0 = self.archive.get_random_elite()[0]
self.opt.reset(new_x0)
self._restarts += 1
class ImprovementEmitter(EmitterBase):
"""Adapts a covariance matrix towards changes in the archive.
This emitter originates in `Fontaine 2020
<https://arxiv.org/abs/1912.02400>`_. Initially, it starts at ``x0`` and
uses CMA-ES to search for solutions that improve the archive, i.e. solutions
that add new entries to the archive or improve existing entries. Once CMA-ES
restarts (see ``restart_rule``), the emitter starts from a randomly chosen
elite in the archive and continues searching for solutions that improve the
archive.
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.
sigma0 (float): Initial step size.
selection_rule ("mu" or "filter"): Method for selecting solutions in
CMA-ES. With "mu" selection, the first half of the solutions will be
selected, while in "filter", any solutions that were added to the
archive will be selected.
restart_rule ("no_improvement" or "basic"): Method to use when checking
for restart. 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.
weight_rule ("truncation" or "active"): Method for generating weights in
CMA-ES. Either "truncation" (positive weights only) or "active"
(include negative weights).
bounds (None or array-like): Bounds of the solution space. Solutions are
clipped to these bounds. Pass None to indicate there are no bounds.
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 send back in :meth:`ask`.
If not passed in, a batch size will automatically be calculated.
seed (int): Value to seed the random number generator. Set to None to
avoid a fixed seed.
Raises:
ValueError: If any of ``selection_rule``, ``restart_rule``, or
``weight_rule`` is invalid.
"""
def __init__(self,
archive,
x0,
sigma0,
selection_rule="filter",
restart_rule="no_improvement",
weight_rule="truncation",
bounds=None,
batch_size=None,
seed=None):
self._x0 = np.array(x0, dtype=archive.dtype)
self._sigma0 = sigma0
EmitterBase.__init__(
self,
archive,
len(self._x0),
bounds,
batch_size,
seed,
)
if selection_rule not in ["mu", "filter"]:
raise ValueError(f"Invalid selection_rule {selection_rule}")
self._selection_rule = selection_rule
if restart_rule not in ["basic", "no_improvement"]:
raise ValueError(f"Invalid restart_rule {restart_rule}")
self._restart_rule = restart_rule
opt_seed = None if seed is None else self._rng.integers(10_000)
self.opt = CMAEvolutionStrategy(sigma0, batch_size, self._solution_dim,
weight_rule, opt_seed,
self._archive.dtype)
self.opt.reset(self._x0)
self._num_parents = (self.opt.batch_size //
2 if selection_rule == "mu" else None)
self._batch_size = self.opt.batch_size
self._restarts = 0 # Currently not exposed publicly.
@property
def x0(self):
"""numpy.ndarray: Initial solution for the optimizer."""
return self._x0
@property
def sigma0(self):
"""float: Initial step size for the CMA-ES optimizer."""
return self._sigma0
def ask(self):
"""Samples new solutions from a multivariate Gaussian.
The multivariate Gaussian is parameterized by the CMA-ES optimizer.
Returns:
``(self.batch_size, self.solution_dim)`` array -- contains
``batch_size`` 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.
"""
if self._restart_rule == "no_improvement":
return num_parents == 0
return False
def tell(self, solutions, objective_values, behavior_values):
"""Gives the emitter results from evaluating solutions.
As solutions are inserted into the archive, we record their "improvement
value" -- conveniently, this is the ``value`` returned by
:meth:`ribs.archives.ArchiveBase.add`. We then rank the solutions
according to their add status (new solutions rank in front of
solutions that improved existing entries in the archive, which rank
ahead of solutions that were not added), followed by their improvement
value. We then pass the ranked solutions to the underlying CMA-ES
optimizer to update the search parameters.
Args:
solutions (numpy.ndarray): Array of solutions generated by this
emitter's :meth:`ask()` method.
objective_values (numpy.ndarray): 1D array containing the objective
function value of each solution.
behavior_values (numpy.ndarray): ``(n, <behavior space dimension>)``
array with the behavior space coordinates of each solution.
"""
ranking_data = []
new_sols = 0
for i, (sol, obj, beh) in enumerate(
zip(solutions, objective_values, behavior_values)):
status, value = self._archive.add(sol, obj, beh)
ranking_data.append((status, value, i))
if status in (AddStatus.NEW, AddStatus.IMPROVE_EXISTING):
new_sols += 1
# New solutions sort ahead of improved ones, which sort ahead of ones
# that were not added.
ranking_data.sort(reverse=True)
indices = [d[2] for d in ranking_data]
num_parents = (new_sols if self._selection_rule == "filter" else
self._num_parents)
self.opt.tell(solutions[indices], num_parents)
# Check for reset.
if (self.opt.check_stop([value for status, value, i in ranking_data])
or self._check_restart(new_sols)):
new_x0 = self._archive.get_random_elite()[0]
self.opt.reset(new_x0)
self._restarts += 1
