## Revision 2071c852751535b6df1d28671a9dbb31a4fb600d (click the page title to view the current version)

# Tensorflow for Reinformcement Learning

**Slides** rl-3.pdf

## Task 1 - Tensorflow/Keras

We will start today with getting used to tensorflow/keras, you can also adapt the exercises to pytorch or similar if prefered (but code-examples will be given in tensorflow).

First, install tensorflow:

We will only be using the Keras API, you can find the documentation here

Verify in python with:

### Part A - Perceptron

We can make a single perceptron with keras like this:

```
from tensorflow import keras
from tensorflow.python.keras.layers import Dense
model = tf.keras.Sequential([
Dense(units=1, input_dim=1)
])
```

and do a forward propagation with:

Furthermore, we can get and set the current weights with:

```
# Get weights
model.layers[0].get_weights()
# Set weights (TODO: replace w1 and b1)
model.layers[0].set_weights([np.array([[w1]]), np.array([b1]))
```

**Tasks/Questions** - Test out different values for the weight and bias - How do you forward-propogate multiple values at once? - Can you plot the graph for some range of inputs?

## Task 2 - Q-Values from an ANN

We still want to work with Q-values, meaning that we would like some value for all possible actions as output from our neural network. Our FrozenLake environment has 4 possible actions, and we already know the q-values for all possible states, making it easy to fit a neural network.

### Part A - Creating a network

The following code will create a neural network that inputs a state (one value) and outputs 4 values (one for each action), it will also assume 16 possible states (0-15):

```
import numpy as np
import tensorflow as tf
from tensorflow import keras
from tensorflow.python.keras.layers import Dense
x_data = np.linspace(0, 15, 16)
normalizer = keras.layers.Normalization(input_shape=[1,], axis=None)
normalizer.adapt(np.array(x_data))
model = keras.Sequential([
normalizer,
Dense(64, activation='relu'),
Dense(64, activation='relu'),
Dense(4)
])
model.compile(
optimizer=tf.optimizers.Adam(learning_rate=0.001),
loss='mse'
)
```

Some of this code can be safely ignored (normalization and the compile method).

**Tasks/Questions** - What is the design of this neural network?

### Part B - Training

As we already have Q-Values, let us train the network on the data:

```
y_data = np.array([
[0.54, 0.53, 0.53, 0.52],
[0.34, 0.33, 0.32, 0.50],
[0.44, 0.43, 0.42, 0.47],
[0.31, 0.31, 0.30, 0.46],
[0.56, 0.38, 0.37, 0.36],
[0., 0., 0., 0.],
[0.36, 0.2, 0.36, 0.16],
[0., 0., 0., 0.],
[0.38, 0.41, 0.40, 0.59],
[0.44, 0.64, 0.45, 0.40],
[0.62, 0.50, 0.40, 0.33],
[0., 0., 0., 0.],
[0., 0., 0., 0.],
[0.46, 0.53, 0.74, 0.50],
[0.73, 0.86, 0.82, 0.78],
[1, 1, 1, 1]
])
model.fit(
x_data,
y_data,
epochs=50000,
verbose=0)
```

**Tasks/Questions** - Test out the forward propagation, are the values similar to what you expect from a Q-table? - Plot the utility given optimal play.

### Part C - FrozenLake

Given the model trained above and an optimal policy (argmax of output), can you move around the environment/solve the problem?

## Task 3 - DQN

Given exercises from last week, we now only need an implementation of a replay-buffer to implement a DQN (Deep Q-network) agent. The replay-buffer needs two methods, one to store experiences (state, action, reward, next_state), and one to sample from the replay-buffer.

**Implement these two methods**

## Task 4 - MountainCar

Until now we have been working on the FrozenLake environment. Try to solve the MountainCar environment using techniques we have learned in this course. I will update this page with a DQN-solution later (hopefully before the end of the day).