ConvNetJS Deep Q Learning Reinforcement Learning with Neural Network demo

Демонстрація глибокого Q-навчання від ConvNetJS

var num_inputs = 27; // 9 eyes, each sees 3 numbers (wall, green, red thing proximity)
var num_actions = 5; // 5 possible angles agent can turn
var temporal_window = 1; // amount of temporal memory. 0 = agent lives in-the-moment :)
var network_size = num_inputs*temporal_window + num_actions*temporal_window + num_inputs;

// the value function network computes a value of taking any of the possible actions
// given an input state. Here we specify one explicitly the hard way
// but user could also equivalently instead use opt.hidden_layer_sizes = [20,20]
// to just insert simple relu hidden layers.
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: 50, activation:'relu'});
layer_defs.push({type:'fc', num_neurons: 50, activation:'relu'});
layer_defs.push({type:'regression', num_neurons:num_actions});

// options for the Temporal Difference learner that trains the above net
// by backpropping the temporal difference learning rule.
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 = 30000;
opt.start_learn_threshold = 1000;
opt.gamma = 0.7;
opt.learning_steps_total = 200000;
opt.learning_steps_burnin = 3000;
opt.epsilon_min = 0.05;
opt.epsilon_test_time = 0.05;
opt.layer_defs = layer_defs;
opt.tdtrainer_options = tdtrainer_options;

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