class_docstr: Play tictactoe against a randomly acting opponent
class_docstr_tok: ["Play","tictactoe","against","a","randomly","acting","opponent"]
code: def step(self, action):
      self._state = copy.deepcopy(self._state)
      row = action // 3
      col = action % 3
  
      # Illegal move -- the square is not empty
      if not np.all(
          self._state[0][row][col] == TicTacToeEnvironment.EMPTY
      ):
          self._terminated = True
          return TicTacToeEnvironment.ILLEGAL_MOVE_PENALTY
  
      # Move X
      self._state[0][row][col] = TicTacToeEnvironment.X
  
      # Did X Win
      if self.check_winner(TicTacToeEnvironment.X):
          self._terminated = True
          return TicTacToeEnvironment.WIN_REWARD
  
      if self.game_over():
          self._terminated = True
          return TicTacToeEnvironment.DRAW_REWARD
  
      move = self.get_O_move()
      self._state[0][move[0]][move[1]] = TicTacToeEnvironment.O
  
      # Did O Win
      if self.check_winner(TicTacToeEnvironment.O):
          self._terminated = True
          return TicTacToeEnvironment.LOSS_PENALTY
  
      if self.game_over():
          self._terminated = True
          return TicTacToeEnvironment.DRAW_REWARD
      return TicTacToeEnvironment.NOT_LOSS
  
file_path: deepchem/deepchem/rl/envs/tictactoe.py
imports: import numpy
  import copy
  import random
  import deepchem
label_desc: Use image node_id 3 for calling the TicTacToeEnvironment obj's underlying member method code with example usage: obj.step(action) and returns: TicTacToeEnvironment, TicTacToeEnvironment, TicTacToeEnvironment, TicTacToeEnvironment, TicTacToeEnvironment, TicTacToeEnvironment