original_docstring: 
          Simulate a random game except that we play all known critical
          cells first, return the winning player and record critical cells at the end.
  
          
docstring: Simulate a random game except that we play all known critical
  cells first, return the winning player and record critical cells at the end.
docstring_tokens: Simulate a random game except that we play all known critical cells first return the winning player and record critical cells at the end .
code: def roll_out(self, state: GameState) -> tuple:
          """
          Simulate a random game except that we play all known critical
          cells first, return the winning player and record critical cells at the end.
  
          """
          moves = state.moves()
          black_rave_moves = sorted(self.black_rave.keys(),
                                    key=lambda cell: self.black_rave[cell])
          white_rave_moves = sorted(self.white_rave.keys(),
                                    key=lambda cell: self.white_rave[cell])
          black_pool = []
          white_pool = []
  
          i = 0
          while len(black_pool) < MCTSMeta.POOLRAVE_CAPACITY and i < len(black_rave_moves):
              if black_rave_moves[i] in moves:
                  black_pool.append(black_rave_moves[i])
              i += 1
          i = 0
          while len(white_pool) < MCTSMeta.POOLRAVE_CAPACITY and i < len(white_rave_moves):
              if white_rave_moves[i] in moves:
                  white_pool.append(white_rave_moves[i])
              i += 1
          num_pool = 0
          while state.winner == GameMeta.PLAYERS["none"]:
              move = None
              if len(black_pool) > 0 and state.turn() == GameMeta.PLAYERS["black"]:
                  move = choice(black_pool)
                  num_pool += 1
              elif len(white_pool) > 0:
                  move = choice(white_pool)
                  num_pool += 1
              if random() > MCTSMeta.RANDOMNESS or not move or move not in moves:
                  move = choice(moves)
                  num_pool -= 1
  
              state.play(move)
              moves.remove(move)
  
          black_rave_pts = []
          white_rave_pts = []
  
          for x in range(state.size):
              for y in range(state.size):
                  if state.board[(x, y)] == GameMeta.PLAYERS["black"]:
                      black_rave_pts.append((x, y))
                      if state.winner == GameMeta.PLAYERS["black"]:
                          if (x, y) in self.black_rave:
                              self.black_rave[(x, y)] += 1
                          else:
                              self.black_rave[(x, y)] = 1
                      else:
                          if (x, y) in self.black_rave:
                              self.black_rave[(x, y)] -= 1
                          else:
                              self.black_rave[(x, y)] = -1
                  elif state.board[(x, y)] == GameMeta.PLAYERS["white"]:
                      white_rave_pts.append((x, y))
                      if state.winner == GameMeta.PLAYERS["white"]:
                          if (x, y) in self.white_rave:
                              self.white_rave[(x, y)] += 1
                          else:
                              self.white_rave[(x, y)] = 1
                      else:
                          if (x, y) in self.white_rave:
                              self.white_rave[(x, y)] -= 1
                          else:
                              self.white_rave[(x, y)] = -1
  
          return state.winner, black_rave_pts, white_rave_pts
code_tokens: def roll_out ( self , state : GameState ) -> tuple : moves = state . moves ( ) black_rave_moves = sorted ( self . black_rave . keys ( ) , key = lambda cell : self . black_rave [ cell ] ) white_rave_moves = sorted ( self . white_rave . keys ( ) , key = lambda cell : self . white_rave [ cell ] ) black_pool = [ ] white_pool = [ ] i = 0 while len ( black_pool ) < MCTSMeta . POOLRAVE_CAPACITY and i < len ( black_rave_moves ) : if black_rave_moves [ i ] in moves : black_pool . append ( black_rave_moves [ i ] ) i += 1 i = 0 while len ( white_pool ) < MCTSMeta . POOLRAVE_CAPACITY and i < len ( white_rave_moves ) : if white_rave_moves [ i ] in moves : white_pool . append ( white_rave_moves [ i ] ) i += 1 num_pool = 0 while state . winner == GameMeta . PLAYERS [ "none" ] : move = None if len ( black_pool ) > 0 and state . turn ( ) == GameMeta . PLAYERS [ "black" ] : move = choice ( black_pool ) num_pool += 1 elif len ( white_pool ) > 0 : move = choice ( white_pool ) num_pool += 1 if random ( ) > MCTSMeta . RANDOMNESS or not move or move not in moves : move = choice ( moves ) num_pool -= 1 state . play ( move ) moves . remove ( move ) black_rave_pts = [ ] white_rave_pts = [ ] for x in range ( state . size ) : for y in range ( state . size ) : if state . board [ ( x , y ) ] == GameMeta . PLAYERS [ "black" ] : black_rave_pts . append ( ( x , y ) ) if state . winner == GameMeta . PLAYERS [ "black" ] : if ( x , y ) in self . black_rave : self . black_rave [ ( x , y ) ] += 1 else : self . black_rave [ ( x , y ) ] = 1 else : if ( x , y ) in self . black_rave : self . black_rave [ ( x , y ) ] -= 1 else : self . black_rave [ ( x , y ) ] = - 1 elif state . board [ ( x , y ) ] == GameMeta . PLAYERS [ "white" ] : white_rave_pts . append ( ( x , y ) ) if state . winner == GameMeta . PLAYERS [ "white" ] : if ( x , y ) in self . white_rave : self . white_rave [ ( x , y ) ] += 1 else : self . white_rave [ ( x , y ) ] = 1 else : if ( x , y ) in self . white_rave : self . white_rave [ ( x , y ) ] -= 1 else : self . white_rave [ ( x , y ) ] = - 1 return state . winner , black_rave_pts , white_rave_pts
short_docstring: Simulate a random game except that we play all known critical
  cells first, return the winning player and record critical cells at the end.
short_docstring_tokens: Simulate a random game except that we play all known critical cells first return the winning player and record critical cells at the end .
comment: """
          Simulate a random game except that we play all known critical
          cells first, return the winning player and record critical cells at the end.
  
          """