class_docstr: 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.
class_docstr_tok: ["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","."]
code: 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
  
file_path: tensorforce/examples/multiactor_environment.py
imports: import numpy
  from tensorforce import Environment, Runner
label_desc: Use image node_id 6 for calling the MultiactorEnvironment obj's underlying member method code with example usage: obj.execute(actions) and returns: self,  states,  terminal,  reward