![]() ![]() Keep asking the user to enter a row and col until they are valid.Keep track of the player’s turn and what symbol they are using.Create a function that draws the board like a square.Create a Tic Tac Toe board and fill it with dashes.Once the player wins by getting 3 in a row, column, or diagonal, the program prints that player 1 or 2 has won and prints out the final board.If the position the player entered is “off the board” or already has an x or o on it, then our program notifies the player, who is prompted to enter another row and col.Each turn it asks either player 1 or player 2 to enter a row and col index which is where they want to place their x and o, and then the board is printed again with the x or o in the right spot.After asking us for our names, the program prints out a 3×3 board filled with dashes, signifying empty spots.You can also view my project solution code if you get stuck. Without further ado, let’s get started! Project DemoĬlick run to play the Tic Tac Toe Java game yourself below! Moreover, we learn how we can turn a commonly played game into code by learning to think like a programmer. Just using these core concepts, we can create a whole game of Tic Tac Toe from start to finish. Learning Outcomes Core concepts practiced: to ( device ) if step % target_update = 0 : target. tensor (, device = device )) state = next_state optimize_model ( device = device, optimizer = optimizer, policy = policy, target = target, memory = memory, batch_size = batch_size, gamma = gamma, ) if done : state = torch. push ( state, action, next_state, torch. to ( device ) if done : next_state = None memory. step ( select_dummy_action ( next_state )) next_state = torch. If not done : next_state, _, done, _ = env. clip ( step / eps_steps, 0, 1 ) eps = ( 1 - t ) * eps_start + t * eps_end action, was_random = select_model_action ( device, policy, state, eps ) if was_random : _randoms += 1 next_state, reward, done, _ = env. to ( device ) for step in range ( n_steps ): t = np. parameters (), lr = 1e-3 ) memory = ReplayMemory ( 50_000 ) env = TicTacToe () state = torch. The random player picks a random legal move each turn: To account for this, this time around I’ve set the agent up to play against a random player. In practice, self-play of this form can lead to non-optimal strategy learning as the model learns how to beat itself, rather than the optimal player. In the keras-rl implementation, I handled this through self-play: player 2 was a copy of the model, operating under the same conditions as player 1. In addition, we need a mechanism to handle the actions of player 2. I decided to use a linearly annealed epsilon greedy policy (in which, during training, the model chooses a random action with probability eps, and this parameter is linearly interpolated toward a minimum value). Where we diverge from the tutorial is in our training loop: ![]() smooth_l1_loss ( state_action_values, expected_state_action_values. detach () # Compute the expected Q valuesĮxpected_state_action_values = ( next_state_values * gamma ) + reward_batch # Compute Huber loss zeros ( batch_size, device = device ) next_state_values = target ( non_final_next_states ). # state value or 0 in case the state was final. # This is merged based on the mask, such that we'll have either the expected # on the "older" target_net selecting their best reward with max(1). # Expected values of actions for non_final_next_states are computed based In Tic Tac Toe, the space of possible board configurations is discrete and relatively small (specifically, there are $19683 = 3^) for all next states. This is in contrast to on-policy approaches, in which we can only train on actions that have been selected by the current policy. In off-policy approaches, we can incorporate a wide variety of actions and their outcomes into our policy learning, including actions that haven’t been selected by the current policy. I’m choosing this because I want an off-policy batch reinforcement learning algorithm: Once again, we’ll use a deep Q network to learn our policies. Grab the complete code from github here! Setting up the game In this post, I’ll do much the same, except this time I’ll shameless plagiarize the official PyTorch documentation’s DQN tutorial instead. ![]() In this post, we return again to the world’s most challenging game, the apex of strategy and art, a game which has broken minds and machines and left a trail of debris and gibbering madmen along the highway of history.Īvid readers of this blog (hi, mom!) might recall that we previously attempted Tic Tac Toe using a DQN and the Keras-RL package (built on Keras and TensorFlow). ![]()
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