Source code for tensorforce.core.optimizers.meta_optimizer_wrapper

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from tensorforce.core.optimizers import MetaOptimizer


[docs]class MetaOptimizerWrapper(MetaOptimizer): """ Meta optimizer wrapper (specification key: `meta_optimizer_wrapper`). Args: name (string): Module name (<span style="color:#0000C0"><b>internal use</b></span>). optimizer (specification): Optimizer configuration (<span style="color:#C00000"><b>required</b></span>). multi_step (parameter, int > 0): Number of optimization steps (<span style="color:#00C000"><b>default</b></span>: single step). subsampling_fraction (parameter, 0.0 < float <= 1.0): Fraction of batch timesteps to subsample (<span style="color:#00C000"><b>default</b></span>: no subsampling). clipping_threshold (parameter, float > 0.0): Clipping threshold (<span style="color:#00C000"><b>default</b></span>: no clipping). optimizing_iterations (parameter, int >= 0): Maximum number of line search iterations (<span style="color:#00C000"><b>default</b></span>: no optimizing). summary_labels ('all' | iter[string]): Labels of summaries to record (<span style="color:#00C000"><b>default</b></span>: inherit value of parent module). """ def __init__( self, name, optimizer, multi_step=1, subsampling_fraction=1.0, clipping_threshold=None, optimizing_iterations=0, summary_labels=None, **kwargs ): optimizer = dict(type=optimizer) optimizer.update(kwargs) if optimizing_iterations > 0: optimizer = dict( type='optimizing_step', optimizer=optimizer, ls_max_iterations=optimizing_iterations ) if clipping_threshold is not None: optimizer = dict( type='clipping_step', optimizer=optimizer, threshold=clipping_threshold ) if subsampling_fraction != 1.0: optimizer = dict( type='subsampling_step', optimizer=optimizer, fraction=subsampling_fraction ) if multi_step > 1: optimizer = dict(type='multi_step', optimizer=optimizer, num_steps=multi_step) super().__init__(name=name, optimizer=optimizer, summary_labels=summary_labels) def tf_step(self, variables, **kwargs): return self.optimizer.step(variables=variables, **kwargs)