# Deep Reinforcement

Hula has a variety of options when it comes to deep reinforcement.

Using the `Hula.DeepR`module, we can construct a Neural Network.

```python
from hula.DeepR import Net, FeedForwardLayer
from hula.rlutils import sigmoid, softplus, tanh

ExampleNet = Net(
  FeedForwardLayer(2, 16, tanh),
  FeedForwardLayer(16, 6, softplus),
  FeedForwardLayer(6, 1, sigmoid)
)
```

Next, we need to generate a random action to perform on this network's weights.

```python
ExampleNet.randomAct(0.01)
```

Next, we need to score the network based on how well it did. In this case, let's say we want the network to get closer to `0` for an input of `[0, 1]`.

In hula, higher scores are valued and selected over lower scores. So, in cases where a lower score is preferred, you will have to negate it.

```python
output = ExampleNet.activate([0, 1])[0]

ExampleNet.score(-output)
```

That's all there is to it! 


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