class_docstr: An environment in which an actor performs actions to accomplish a task.
  
  An environment has a current state, which is represented as either a single NumPy
  array, or optionally a list of NumPy arrays.  When an action is taken, that causes
  the state to be updated.  The environment also computes a reward for each action,
  and reports when the task has been terminated (meaning that no more actions may
  be taken).
  
  Two types of actions are supported.  For environments with discrete action spaces,
  the action is an integer specifying the index of the action to perform (out of a
  fixed list of possible actions).  For environments with continuous action spaces,
  the action is a NumPy array.
  
  Environment objects should be written to support pickle and deepcopy operations.
  Many algorithms involve creating multiple copies of the Environment, possibly
  running in different processes or even on different computers.
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docstr: Whether the task has reached its end.
  
  If reset() has not yet been called at least once, this is undefined.
docstr_tok: ["Whether","the","task","has","reached","its","end",".","If","reset","(",")","has","not","yet","been","called","at","least","once",",","this","is","undefined","."]
code: def terminated(self):
      """Whether the task has reached its end.
  
      If reset() has not yet been called at least once, this is undefined.
      """
      return self._terminated
  
label_desc: Use image node_id 3 for calling the Environment obj's underlying member method code with example usage: obj.terminated() and returns: self