code: def test_multiactor_environment(self):
      self.start_tests(name="multi-actor environment")
  
      # ====================
  
      class MultiactorEnvironment(Environment):
          """
          Example multi-actor environment, illustrating best-practice implementation pattern.
  
          State space: position in [0, 10].
          Action space: movement in {-1, 0, 1}.
          Random start in [3, 7].
          Actor 1 perspective as is, actor 2 perspective mirrored.
          Positive reward for being closer to 10.
          """
  
          def __init__(self):
              super().__init__()
  
          def states(self):
              return dict(type="int", num_values=11)
  
          def actions(self):
              return dict(type="int", num_values=3)
  
          def num_actors(self):
              return 2  # Indicates that environment has multiple actors
  
          def reset(self):
              # Always for multi-actor environments: initialize parallel indices
              self._parallel_indices = np.arange(self.num_actors())
  
              # Single shared environment logic, plus per-actor perspective
              self._states = 3 + np.random.randint(5)
              self.second_actor = True
              states = np.stack(
                  [self._states, 10 - self._states], axis=0
              )
  
              # Always for multi-actor environments: return per-actor values
              return self._parallel_indices.copy(), states
  
          def execute(self, actions):
              # Single shared environment logic, plus per-actor perspective
              if self.second_actor:
                  self.second_actor = self.second_actor and not (
                      np.random.random_sample() < 0.1
                  )
                  terminal = np.stack(
                      [False, not self.second_actor], axis=0
                  )
                  delta = (actions[0] - 1) - (actions[1] - 1)
                  self._states = np.clip(
                      self._states + delta, a_min=0, a_max=10
                  )
                  states = np.stack(
                      [self._states, 10 - self._states], axis=0
                  )
              else:
                  terminal = np.stack([False], axis=0)
                  delta = actions[0] - 1
                  self._states = np.clip(
                      self._states + delta, a_min=0, a_max=10
                  )
                  states = np.stack([self._states], axis=0)
              reward = (states - 5.0) / 5.0
  
              # Always for multi-actor environments: update parallel indices, and return per-actor values
              self._parallel_indices = self._parallel_indices[~terminal]
              return (
                  self._parallel_indices.copy(),
                  states,
                  terminal,
                  reward,
              )
  
      # Multi-actor runner, automatically if environment.num_actors() > 1
      runner = Runner(
          agent="benchmarks/configs/ppo.json",
          environment=MultiactorEnvironment,
          max_episode_timesteps=10,
      )
      runner.run(num_episodes=10)
  
      # ====================
  
      self.finished_test()
  
imports: import os
  from tempfile import TemporaryDirectory
  from threading import Thread
  import unittest
  import numpy
  import tensorflow
  from tensorforce import Agent, Environment, Runner
  from test.unittest_base import UnittestBase
label_desc: Use image node_id 7 for calling the TestExamples obj's underlying member method code with example usage: obj.test_multiactor_environment() and returns: dict, dict, int, self,  states, self,  states,  terminal,  reward