nnrl/nn/critic/q_value.py
def initialize_parameters(self, initializer_spec: dict):
        """Initialize each Q estimator in the ensemble.

        Args:
            initializer_spec: Dictionary with mandatory `name` key corresponding
                to the initializer function name in `torch.nn.init` and optional
                keyword arguments.
        """
        for q_value in self:
            q_value.initialize_parameters(initializer_spec)
Initialize each Q estimator in the ensemble.

        Args:
            initializer_spec: Dictionary with mandatory `name` key corresponding
                to the initializer function name in `torch.nn.init` and optional
                keyword arguments.
        
Initialize each Q estimator in the ensemble.
Initialize each Q estimator in the ensemble .
def initialize_parameters(self, initializer_spec: dict):
        for q_value in self:
            q_value.initialize_parameters(initializer_spec)
def initialize_parameters ( self , initializer_spec : dict ) : for q_value in self : q_value . initialize_parameters ( initializer_spec )
Initialize each Q estimator in the ensemble.
Initialize each Q estimator in the ensemble .
"""Initialize each Q estimator in the ensemble.

        Args:
            initializer_spec: Dictionary with mandatory `name` key corresponding
                to the initializer function name in `torch.nn.init` and optional
                keyword arguments.
        """
self
initializer_spec
dict
Dictionary with mandatory `name` key corresponding
to the initializer function name in `torch.nn.init` and optional
keyword arguments.
Dictionary with mandatory ` name ` key corresponding to the initializer function name in ` torch . nn . init ` and optional keyword arguments .