import tensorflow as tf

net = DenseLayer(net, n_units=4, act=tf.identity,
    W_init=tf.random_uniform_initializer(0, 0.01), b_init=None, name='q_a_s')
y = net.outputs             # action-value / rewards of 4 actions
predict = tf.argmax(y, 1)   # chose action greedily with reward. in Q-Learning, policy is greedy, so we use "max" to select the next action.

## Below we obtain the loss by taking the sum of squares difference between the target and prediction Q values.
