def __init__(
        self, ewc_lambda: float = 0.1, ewc_alpha: float = 0.9, delta_t: int = 10
    ):
        """
        :param ewc_lambda: hyperparameter to weigh the penalty inside the total
               loss. The larger the lambda, the larger the regularization.
        :param ewc_alpha: Specify the moving average factor for the importance
                matrix, as defined RWalk paper (a.k.a. EWC++). Higher values
                lead to higher weight to newly computed importances. Must be
                in [0, 1]. Defaults to 0.9.
        :param delta_t: Specify the iterations interval in which the parameter
                scores are updated. Defaults to 10.
        """

        super().__init__()

        assert 0 <= ewc_alpha <= 1, "`ewc_alpha` must be in [0, 1]."
        assert delta_t >= 1, "`delta_t` must be at least 1."

        self.ewc_alpha = ewc_alpha
        self.ewc_lambda = ewc_lambda
        self.delta_t = delta_t

        # Information computed every delta_t
        self.checkpoint_params: Dict[str, ParamData] = dict()
        self.checkpoint_loss: Dict[str, ParamData] = dict()

        # Partial scores (s_t1^t2) computed incrementally every delta_t
        self.checkpoint_scores: Dict[str, ParamData] = dict()

        # Information stored at the beginning of every iteration
        self.iter_grad: Dict[str, ParamData] = dict()
        self.iter_importance: Optional[Dict[str, ParamData]] = None
        self.iter_params: Dict[str, ParamData] = dict()

        # Information stored at the end of every experience (t_k in the paper)
        self.exp_scores: Optional[Dict[str, ParamData]] = None
        self.exp_params: Optional[Dict[str, ParamData]] = None
        self.exp_importance: Optional[Dict[str, ParamData]] = None
        self.exp_penalties: Optional[Dict[str, ParamData]] = None

        :param ewc_lambda: hyperparameter to weigh the penalty inside the total
               loss. The larger the lambda, the larger the regularization.
        :param ewc_alpha: Specify the moving average factor for the importance
                matrix, as defined RWalk paper (a.k.a. EWC++). Higher values
                lead to higher weight to newly computed importances. Must be
                in [0, 1]. Defaults to 0.9.
        :param delta_t: Specify the iterations interval in which the parameter
                scores are updated. Defaults to 10.
        
avalanche/training/plugins/rwalk.py