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 argmax_with_mask(self, q_values, possible_actions_mask):
      # Set q-values of impossible actions to a very large negative number.
      q_values = q_values.reshape(possible_actions_mask.shape)
      q_values = q_values + self.ACTION_NOT_POSSIBLE_VAL * (
          1 - possible_actions_mask
      )
      return q_values.argmax(1)
  
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 5 for calling the QRDQNTrainer obj's underlying member method code with example usage: obj.argmax_with_mask(q_values, possible_actions_mask) and returns: q_values