original_docstring: 
          Encodes the state vector with the following vector - (S, O, E).
          This is done so that the learning remains consistent across games.
          There is a clear distinction between the player's own pieces and
          opponents pieces irrespective of the player_letter chosen(X / O).
          S - Self
          O - Opponent
          E - Empty
          :return: encoded state vector
          
docstring: Encodes the state vector with the following vector - (S, O, E).
  This is done so that the learning remains consistent across games.
  There is a clear distinction between the player's own pieces and
  opponents pieces irrespective of the player_letter chosen(X / O).
  S - Self
  O - Opponent
  E - Empty
docstring_tokens: Encodes the state vector with the following vector - ( S O E ) . This is done so that the learning remains consistent across games . There is a clear distinction between the player ' s own pieces and opponents pieces irrespective of the player_letter chosen ( X / O ) . S - Self O - Opponent E - Empty
code: def __encode_state(self, state):
          """
          Encodes the state vector with the following vector - (S, O, E).
          This is done so that the learning remains consistent across games.
          There is a clear distinction between the player's own pieces and
          opponents pieces irrespective of the player_letter chosen(X / O).
          S - Self
          O - Opponent
          E - Empty
          :return: encoded state vector
          """
          encoded_state = []
          for s in state:
              if s == self.letter:
                  encoded_state.append('S')
              elif s == self.enemy_letter:
                  encoded_state.append('O')
              else:
                  encoded_state.append('E')
  
          return encoded_state
code_tokens: def __encode_state ( self , state ) : encoded_state = [ ] for s in state : if s == self . letter : encoded_state . append ( 'S' ) elif s == self . enemy_letter : encoded_state . append ( 'O' ) else : encoded_state . append ( 'E' ) return encoded_state
short_docstring: Encodes the state vector with the following vector - (S, O, E).
short_docstring_tokens: Encodes the state vector with the following vector - ( S O E ) .
comment: """
          Encodes the state vector with the following vector - (S, O, E).
          This is done so that the learning remains consistent across games.
          There is a clear distinction between the player's own pieces and
          opponents pieces irrespective of the player_letter chosen(X / O).
          S - Self
          O - Opponent
          E - Empty
          :return: encoded state vector
          """