 = low). The action taken by the agent (the medium-sized circle moving around) is shown by the arrow.

### Deep Q Learning

We're going to extend the Temporal Difference Learning (Q-Learning) to continuous state spaces. In the previos demo we've estimated the Q learning function \\(Q(s,a)\\) as a lookup table. Now, we are going to use a function approximator to model \\(Q(s,a) = f\_{\theta}(s,a)\\), where \\(\theta\\) are some parameters (e.g. weights and biases of a neurons in a network). However, as we wi