2048 playing agent using deep Q-learning in Matlab..zip

# Matlab 2048 An agent playing game [2048](http://gabrielecirulli.github.io/2048/) using deep Q-learning in Matlab. **NB!** I never got this code to learn too well, improvements are welcome! How to download the code: ``` git--recursive clone https://github.com/tambetm/matlab2048.git ``` The code uses my fork of [DeepLearnToolbox](https://github.com/rasmusbergpalm/DeepLearnToolbox) to implement neural network. How to run it: ``` clear all; rng('shuffle'); % Add DeepLearnToolbox to path addpath(genpath('DeepLearnToolbox')); % How many games to play n = 100; % Number of groups for averaging k = 10; % Creates new agent with following parameters: opts.exploration_steps = 0; opts.exploration_rate = 0.05; opts.discount_rate = 0; opts.learning_rate = 0.001; opts.momentum = 0.95; opts.layers = [1000]; opts.preprocess = @(x) log2(max(x, 1)); opts.activation_function = 'relu'; opts.dropout_fraction = 0; opts.weight_penalty = 0; opts.minibatch_size = 100; a = NNAgent(opts); % Plays n games results_nn = a.play(n); % Plays n games by making random moves b = RandomAgent(); results_random = b.play(n); % Plot results. figure; results = reshape([results_nn; results_random], 2, k, n/k); errorbar(mean(results, 3)', std(results, 0, 3)'); legend('NNAgent', 'RandomAgent'); ``` To see the moves agent makes and predicted Q-values play just one game: ``` EDU>> a.play(1) 0 0 0 0 0 0 0 2 0 0 0 0 0 2 0 0 DOWN(random) Reward: 0 0 0 0 0 0 0 0 0 0 0 2 0 0 2 0 2 Q-values: 53.3039 49.4 51.5175 50.7218 UP(predicted) Reward: 0 0 2 2 2 0 0 0 0 0 0 2 0 0 0 0 0 Q-values: 62.8255 62.2575 72.6659 63.6495 DOWN(predicted) Reward: 4 0 0 0 0 0 0 0 2 0 0 0 0 0 2 4 2 ... Q-values: 64.0637 65.0713 65.0745 64.7698 DOWN(predicted) Reward: 4 2 4 2 8 4 64 8 16 8 16 2 4 16 4 32 2 1 616 ans = 616 ``` Q-values are in the order of UP, RIGHT, DOWN, LEFT.