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 num_actors(self):
      return 2
  
file_path: tensorforce/examples/multiactor_environment.py
imports: import numpy
  from tensorforce import Environment, Runner
label_desc: Use image node_id 4 for calling the MultiactorEnvironment obj's underlying member method code with example usage: obj.num_actors() and returns: int