class_docstr: Multi-objective MPO loss with decoupled KL constraints.
  
  This implementation of the MO-MPO loss is based on the approach proposed in
  (Abdolmaleki, Huang et al., 2020). The following features are included as
  options:
  - Satisfying the KL-constraint on a per-dimension basis (on by default)
  
  (Abdolmaleki, Huang et al., 2020): https://arxiv.org/pdf/2005.07513.pdf
class_docstr_tok: ["Multi-objective","MPO","loss","with","decoupled","KL","constraints",".","This","implementation","of","the","MO-MPO","loss","is","based","on","the","approach","proposed","in","(","Abdolmaleki",",","Huang","et","al.",",","2020",")",".","The","following","features","are","included","as","options",":","-","Satisfying","the","KL-constraint","on","a","per-dimension","basis","(","on","by","default",")","(","Abdolmaleki",",","Huang","et","al.",",","2020",")",":","https",":","\/\/arxiv.org\/pdf\/2005.07513.pdf"]
code: def objective_names(self):
      return self._objective_names
  
file_path: acme/acme/tf/losses/mompo.py
imports: import dataclasses
  from typing import Dict, Sequence, Tuple, Union
  from acme.tf.losses import mpo
  import sonnet
  import tensorflow
  import tensorflow_probability
label_desc: Use image node_id 2 for calling the MultiObjectiveMPO obj's underlying member method code with example usage: obj.objective_names() and returns: self