class_docstr: Example vectorized environment, illustrating best-practice implementation pattern.
  
  State space: position in [0, 10].
  Action space: movement in {-1, 0, 1}.
  Random start in [0, 3] or [7, 10].
  Positive reward for moving towards the center 5.
class_docstr_tok: ["Example","vectorized","environment",",","illustrating","best-practice","implementation","pattern",".","State","space",":","position","in","[","0",",","10","]",".","Action","space",":","movement","in","{","-1",",","0",",","1","}",".","Random","start","in","[","0",",","3","]","or","[","7",",","10","]",".","Positive","reward","for","moving","towards","the","center","5","."]
code: def __init__(self):
      super().__init__()
  
file_path: tensorforce/examples/vectorized_environment.py
imports: import numpy
  from tensorforce import Environment, Runner
label_desc: Use image node_id 1 to create a new VectorizedEnvironment object from inherited base classes: Environment with example: obj = VectorizedEnvironment()