hunk.code_window:         ):
               self._spaces_in_preferred_format = (
                   self._check_if_space_maps_agent_id_to_sub_space()
               )
           if self._spaces_in_preferred_format:
               return self.observation_space.contains(x)
           logger.warning("observation_space_contains() has not been implemented")
           return True
       @ExperimentalAPI
  
hunk.labels: keep keep keep keep keep replace keep keep keep keep keep
hunk.after_edit:             for key, agent_obs in x.items():
                   if not self.observation_space[key].contains(agent_obs):
                       return False
               if not all(k in self.observation_space for k in x):
                   if log_once("possibly_bad_multi_agent_dict_missing_agent_observations"):
                       logger.warning(
                           "You environment returns observations that are "
                           "MultiAgentDicts with incomplete information. "
                           "Meaning that they only contain information on a subset of"
                           " participating agents. Ignore this warning if this is "
                           "intended, for example if your environment is a turn-based "
                           "simulation."
                       )
               return True
  
hunk.file_path: rllib/env/multi_agent_env.py
hunk.type: replace
file: """This example demonstrates the usage of Dragonfly with Ray Tune.
  
  It also checks that it is usable with a separate scheduler.
  
  Requires the Dragonfly library to be installed (`pip install dragonfly-opt`).
  """
  from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  
  import numpy as np
  import time
  
  from ray import tune
  from ray.tune.suggest import ConcurrencyLimiter
  from ray.tune.schedulers import AsyncHyperBandScheduler
  from ray.tune.suggest.dragonfly import DragonflySearch
  
  
  def objective(config):
      for i in range(config["iterations"]):
          vol1 = config["LiNO3_vol"]  # LiNO3
          vol2 = config["Li2SO4_vol"]  # Li2SO4
          vol3 = config["NaClO4_vol"]  # NaClO4
          vol4 = 10 - (vol1 + vol2 + vol3)  # Water
          # Synthetic functions
          conductivity = vol1 + 0.1 * (vol2 + vol3) ** 2 + 2.3 * vol4 * (vol1 ** 1.5)
          # Add Gaussian noise to simulate experimental noise
          conductivity += np.random.normal() * 0.01
          tune.report(timesteps_total=i, objective=conductivity)
          time.sleep(0.02)
  
  
  if __name__ == "__main__":
      import argparse
  
      parser = argparse.ArgumentParser()
      parser.add_argument(
          "--smoke-test", action="store_true", help="Finish quickly for testing"
      )
      parser.add_argument(
          "--server-address",
          type=str,
          default=None,
          required=False,
          help="The address of server to connect to if using Ray Client.",
      )
      args, _ = parser.parse_known_args()
  
      if args.server_address:
          import ray
  
          ray.init(f"ray://{args.server_address}")
  
      # Optional: Pass the parameter space yourself
      # space = [{
      #     "name": "LiNO3_vol",
      #     "type": "float",
      #     "min": 0,
      #     "max": 7
      # }, {
      #     "name": "Li2SO4_vol",
      #     "type": "float",
      #     "min": 0,
      #     "max": 7
      # }, {
      #     "name": "NaClO4_vol",
      #     "type": "float",
      #     "min": 0,
      #     "max": 7
      # }]
  
      df_search = DragonflySearch(
          optimizer="bandit",
          domain="euclidean",
          # space=space,  # If you want to set the space manually
      )
      df_search = ConcurrencyLimiter(df_search, max_concurrent=4)
  
      scheduler = AsyncHyperBandScheduler()
      analysis = tune.run(
          objective,
          metric="objective",
          mode="max",
          name="dragonfly_search",
          search_alg=df_search,
          scheduler=scheduler,
          num_samples=10 if args.smoke_test else 50,
          config={
              "iterations": 100,
              "LiNO3_vol": tune.uniform(0, 7),
              "Li2SO4_vol": tune.uniform(0, 7),
              "NaClO4_vol": tune.uniform(0, 7),
          },
      )
  
      print("Best hyperparameters found were: ", analysis.best_config)
  