fragile.optimize.evotorch
Contents
fragile.optimize.evotorch
#
This modes implements an interfacing with the evotorch library.
Module Contents#
Classes#
This environment implements an interface with the evotorch library. |
- class fragile.optimize.evotorch.EvotorchEnv(algorithm, function=None, bounds=None, **kwargs)#
Bases:
fragile.core.env.Function
This environment implements an interface with the evotorch library.
When providing an instance of an evotorch
Searcher
it will wrap it and allow to use all the fragile features on top of evotorch, such as plotting, custom policies, etc.- Parameters
algorithm (evotorch.algorithms.searchalgorithm.SearchAlgorithm) –
function (Optional[callable]) –
bounds (Optional[judo.Bounds]) –
- default_inputs#
- default_outputs = ['observs', 'rewards', 'oobs']#
- property algorithm#
Return the evotorch
SearchAlgorithm
instance used by this environment.- Return type
evotorch.algorithms.searchalgorithm.SearchAlgorithm
- property population#
Access the
SolutionBatch
instance used by the evotorchSearchAlgorithm
.- Return type
evotorch.core.SolutionBatch
- property problem#
Access the
Problem
instance used by the evotorchSearchAlgorithm
.- Return type
evotorch.core.Problem
- property dtype#
Access the dtype used by the evotorch
SearchAlgorithm
for the solution values.
- property eval_dtype#
Access the dtype used by the
SearchAlgorithm
for the solution evaluations.
- property solution_length#
Access the length of the solution used by the evotorch
SearchAlgorithm
.- Return type
- _get_bounds()#
Initialize the
Bounds
instance used by this environment.Extract all the information about the dimensionality and data type of the solutions from the problem instance used by the evotorch
SearchAlgorithm
.- Return type
judo.Bounds
- _get_function()#
Return a function that sets the values of the evotorch
SolutionBatch
with the provided points and iterates the evotorchSearchAlgorithm
to obtain new solutions.
- step(actions, observs, **kwargs)#
Sum the target action to the observations to obtain the new points, and evaluate the reward and boundary conditions.
- Returns
Dictionary containing the information of the new points evaluated.
{"observs": new_points, "rewards": scalar array, "oobs": boolean array}
- reset(inplace=True, root_walker=None, states=None, **kwargs)#
Reset the
Function
to the start of a new episode and updates its internal data.- Parameters
inplace (bool) –
root_walker (Optional[fragile.core.typing.StateData]) –
states (Optional[fragile.core.typing.StateData]) –
- Return type
Union[None, fragile.core.typing.StateData]