model.js

        //<![CDATA[
        
        // a few things don't have var in front of them - they update already existing variables the game needs
        lanesSide = 0;
        patchesAhead = 1;
        patchesBehind = 0;
        trainIterations = 10000;

        var num_inputs = (lanesSide * 2 + 1) * (patchesAhead + patchesBehind);
        var num_actions = 5;
        var temporal_window = 3;
        var network_size = num_inputs * temporal_window + num_actions * temporal_window + num_inputs;

        var layer_defs = [];
        layer_defs.push({
            type: 'input',
            out_sx: 1,
            out_sy: 1,
            out_depth: network_size
        });
        layer_defs.push({
            type: 'fc',
            num_neurons: 1,
            activation: 'relu'
        });
        layer_defs.push({
            type: 'regression',
            num_neurons: num_actions
        });

        var tdtrainer_options = {
            learning_rate: 0.001,
            momentum: 0.0,
            batch_size: 64,
            l2_decay: 0.01
        };

        var opt = {};
        opt.temporal_window = temporal_window;
        opt.experience_size = 3000;
        opt.start_learn_threshold = 500;
        opt.gamma = 0.7;
        opt.learning_steps_total = 10000;
        opt.learning_steps_burnin = 1000;
        opt.epsilon_min = 0.0;
        opt.epsilon_test_time = 0.0;
        opt.layer_defs = layer_defs;
        opt.tdtrainer_options = tdtrainer_options;

        brain = new deepqlearn.Brain(num_inputs, num_actions, opt);

        learn = function (state, lastReward) {
            brain.backward(lastReward);
            var action = brain.forward(state);

            draw_net();
            draw_stats();

            return action;
        }

        //]]>