class_docstr: Implementation of QR-DQN (Quantile Regression Deep Q-Network)
  
  See https://arxiv.org/abs/1710.10044 for details
class_docstr_tok: ["Implementation","of","QR-DQN","(","Quantile","Regression","Deep","Q-Network",")","See","https",":","\/\/arxiv.org\/abs\/1710.10044","for","details"]
code: def huber(self, x):
      return torch.where(x.abs() < 1, 0.5 * x.pow(2), x.abs() - 0.5)
  
file_path: ReAgent/reagent/training/qrdqn_trainer.py
imports: import logging
  from typing import List, Tuple
  import reagent.core.types
  import torch
  from reagent.core.configuration import resolve_defaults
  from reagent.core.dataclasses import field
  from reagent.core.parameters import EvaluationParameters, RLParameters
  from reagent.optimizer import SoftUpdate
  from reagent.optimizer.union import Optimizer__Union
  from reagent.training.dqn_trainer_base import DQNTrainerBaseLightning
label_desc: Use image node_id 6 for calling the QRDQNTrainer obj's underlying member method code with example usage: obj.huber(x) and returns: torch