The following is a general setup of the program. Below is a sample of what a game will play like. The input that will be taken in to specify where to place a mark will be in the format of two integers, which specify the row and column where the mark is to be placed. That process will be continued until one player wins or the board is filled up (indicating that a tie occurred). After placing the mark, we will print the board state again and then ask the other player for their move. Our Tic-Tac-Toe will start out by printing the board, and then asking for input from the first player that will specify where on the board to place that player’s mark. For this tutorial, we will be coding a text-based version of Tic-Tac-Toe. General Outline: There are many ways to implement a game of Tic-Tac-Toe in Java, so before we begin coding, we must think about how we will implement the game specifically. This tutorial assumes that you have knowledge of the basic syntax of Java, and access to a working Java compiler. In this tutorial, we will be looking at how to code a working game of Tic-Tac-Toe in Java. Because of these things, Tic-Tac-Toe is fairly easy to code up. The rules of the game are simple and well-known. Tic-Tac-Toe is a very common game that is fairly easy to play. 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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