docstr: Adds multiple policy.learn_on_(loaded)?_batch() results to this builder.
  
  Args:
      all_policies_results: The results returned by all Policy.learn_on_batch or
          Policy.learn_on_loaded_batch wrapped as a dict mapping policy ID to
          results.
docstr_tok: ["Adds","multiple","policy.learn_on_","(","loaded",")","?","_batch","(",")","results","to","this","builder",".","Args",":","all_policies_results",":","The","results","returned","by","all","Policy.learn_on_batch","or","Policy.learn_on_loaded_batch","wrapped","as","a","dict","mapping","policy","ID","to","results","."]
code: def add_learn_on_batch_results_multi_agent(
      self,
      all_policies_results: Dict,
  ) -> None:
      """Adds multiple policy.learn_on_(loaded)?_batch() results to this builder.
  
      Args:
          all_policies_results: The results returned by all Policy.learn_on_batch or
              Policy.learn_on_loaded_batch wrapped as a dict mapping policy ID to
              results.
      """
      for pid, result in all_policies_results.items():
          if pid != "batch_count":
              self.add_learn_on_batch_results(result, policy_id=pid)
  
file_path: ray/rllib/utils/metrics/learner_info.py
imports: from collections import defaultdict
  import numpy
  import tree
  from typing import Dict
  from ray.rllib.utils.annotations import DeveloperAPI
  from ray.rllib.policy.sample_batch import DEFAULT_POLICY_ID
  from ray.rllib.utils.typing import PolicyID
label_desc: Use image node_id 3 for calling the LearnerInfoBuilder obj's underlying member method code with example usage: obj.add_learn_on_batch_results_multi_agent(all_policies_results) without return types