Created
September 9, 2018 20:15
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Training code for tic tac toe solver
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| bot1_sym = 'O' | |
| bot2_sym = 'X' | |
| def optimize_bot(game, bot1, bot2): | |
| """ | |
| Punish or Reward the bot with respect to the agent that wins the game | |
| """ | |
| if game.winner == bot1_sym: | |
| bot1.on_reward(1) | |
| # reward | |
| bot2.on_reward(-1) | |
| # punishment | |
| elif game.winner == bot2_sym: | |
| bot1.on_reward(-1) | |
| bot2.on_reward(1) | |
| def train(epochs, bot1, bot2): | |
| bot1_wins = 0 | |
| bot2_wins = 0 | |
| win_trace = pd.DataFrame(data=np.zeros((epochs, 2)), columns=['bot1', 'bot2']) | |
| for i in range(epochs): | |
| print('-' * 100) | |
| print('epoch: {}'.format(i + 1)) | |
| game = Board() | |
| while not game.stale: | |
| # Exit if the board is full | |
| winner = game.player_move(bot1_sym, *bot1.select_move(game.board)) | |
| # Check if the move results in a winner | |
| # winner = None if no one wins, | |
| # winner = 'draw' if no one can win | |
| if winner: | |
| optimize_bot(game, bot1, bot2) | |
| # reward the winner | |
| bot1_wins += 1 | |
| win_trace.set_value(i, 'bot1', 1) | |
| break | |
| elif winner == 'draw': | |
| break | |
| winner = game.player_move(bot2_sym, *bot2.select_move(game.board)) | |
| if winner: | |
| optimize_bot(game, bot1, bot2) | |
| bot2_wins += 1 | |
| win_trace.set_value(i, 'bot2', 1) | |
| break | |
| win_trace[i] = 2 | |
| elif winner == 'draw': | |
| break | |
| return win_trace, bot1_wins, bot2_wins |
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