The presentation discusses how machine learning enables computers to play video games by learning from examples rather than being explicitly programmed. It covers concepts such as neural networks, supervised learning, reinforcement learning, and the challenges of overfitting, illustrating these ideas with examples from spam filtering and image captioning. The speaker also shares personal experiences applying these techniques to play games like Super Mario Bros and highlights the rapid advancements in the field of machine learning.

1. Teaching Your Computer To Play
Video Games
A Presentation For The Bainbridge BARN
September 18, 2016

2. About Me
Tech enthusiast; hardware and software
hacker; particular interest in machine learning
Pros:
This presentation is free of charge!
Cons:
No training in computer science, embedded
systems design, electrical engineering,
software development

3. What Is Machine Learning?
● A way for computers to learn without being
explicitly programmed
● It allows machines to make predictions
about the future after studying examples
from the past
● Forms the basis of artificial intelligence
● One of the hottest areas of computer
science today!

4. Why Video Games?
● They are easy to set up and let you control
every aspect of the learning environment
● They are fun
● They can be directly compared against
human performance
Here’s one of my favorite examples

5. Consider Spam Filtering...

6. Consider Spam Filtering...
● It is impossible to predict every possible way a
spam email could be written...
● You could try programming a bunch of rules:
○ “Cheap Meds From Canada” -> SPAM
○ “Your Medication Has Shipped -> NOT SPAM
● This rapidly becomes intractable - likely to get too
many false positives and false negatives

7. Consider Spam Filtering...
● A better way is to show the machine a bunch of
human-labeled examples, and let it generalize a
way to identify spam from these
● This is called Supervised Learning, because we
train our system on a bunch of examples
● Basis for most spam-filtering systems today

8. So How Does It Work?
● There are many types of algorithms that are used
for learning
● These have colorful names:
○ Naive Baysian Classifiers
○ Support Vector Machines
○ Random Forest Trees
● But we’ll focus here on Neural Networks since they
are currently some of the most widely used and are
so cool

9. Neural Networks
● Neural networks were
inspired from studying how
our brains work
● These consist of multiple
layers of interconnected
nodes (like neurons)
● They take an input (like a
video image), pass it
through, and yield an output
(like a label)

10. Neural Networks
Each of these connections has a weight associated
with it.
.3
.5
.8

11. Neural Networks
Information propagates through each layer of the
network, adjusted by the weights
.3
.5
.8
100

12. Neural Networks
Information propagates through each layer of the
network, adjusted by the weights
.3
.5
.8
100
30
50
80

13. Neural Networks
Information propagates through each layer of the
network, adjusted by the weights
.3
.5
.8
100
30
50
80
30
30
30
50
50
50
80
80
80

14. How Neural Networks Learn
At each step, you compare the actual output (ie - 78%
chance it’s a cat) with the expected output (ie - yes,
it’s a cat)
......
NOT CAT
CAT
Pixel
Value
183
22
78

15. How Neural Networks Learn
The weights are then adjusted to bring the actual
output closer to the expect output. Rinse and repeat...
......
NOT CAT
CAT
Weights
183
20
80

16. Neural Network Learning
● Adjusting these weights is how the network learns
● Real life networks may have millions of weights
spread over many layers
● This process allows the network to learn complex
behaviors and, we hope, an ability to generalize
concepts beyond what it was explicitly taught

17. Neural Network Topology
● There are multiple ways of connecting the nodes in
a neural network
● All of these seek to minimize the number of weights
you need and to combat the central problem of
machine learning: overfitting
● Overfitting means your network performs great so
long as it’s working with data it’s already seen. But
it fails miserably when it needs to generalize to data
it hasn’t seen

18. Neural Networks
● Recently, a type called convolutional neural
networks has been achieving amazing results,
particularly for problems that involve classifying
images or video
● Moreover, when you incorporate many layers (5, 6,
7, and more), the power of these networks is
astounding
● This is where the phrase deep learning comes
from, since these networks have many layers

19. Stanford’s Image Classifier
● Here’s a deep convolutional neural network in
action from a 2014 competition!
● This network was trained on 1.2 million images,
each labeled with one of 1000 categories
● Then was tested on images it had not seen
before…
● And achieved an error rate of only 5.1% compared
with how humans would classify the images
Check it out!

20. Text Generation
● Another type of neural network (called a recurrent
neural network) is great for sequential problems,
like predicting the next word in a sentence: “There
are so many clouds in the ____.”
● A fun trick with these is to train them on a body of
text (like the Bible or the complete works of
Shakespeare) and see what they spit out...

21. Computer-Generated Bible and
Shakespeare Verses
● 1 Chronicles 4:7 Then came them out of the
house of brass; and in the midst is to him, and was
done with him with the new moon: for in the city of
Jeshua ye shall put him speed, as the horn of me
plagued among them that hath need.
● Second Senator: They are away this miseries,
produced upon my soul, Breaking and strongly
should be buried, when I perish The earth and
thoughts of many states.
Source: http://karpathy.github.io/2015/05/21/rnn-effectiveness/

22. Image Captioning
● An even more challenging machine learning task is
automatically generating captions to images
● This often combines deep convolutional networks
with recurrent neural networks
● Here’s an example of Google’s work on this
subject...

23. Image Captioning
Source: https://research.googleblog.com/2014/11/a-picture-is-worth-thousand-coherent.html

24. Image Captioning
This is really starting to approach how our own minds
seem to learn...

25. Unsupervised Learning
● Most examples so far have been of Supervised
Learning, where the machine is trained on a bunch
of human-labeled examples; Unsupervised
Learning is where the human does not provide any
guidance
● Specifically, Reinforcement Learning simply
provides an environment for the machine to play in,
and it is given rewards and penalties based on its
actions
● It’s up to the machine to figure out the best
strategy...

26. Learning To Play Video Games
● This is how we can teach a machine to play video
games:
○ The score is the reward
○ The machine gets to press any buttons it wants
Here’s a video demonstrating Google’s Atari project
(from 9:25)

27. How Reinforcement Learning
Works
● A neural network is at the heart of Reinforcement
Learning
● For video games, the input is the screen itself at
each frame, and the output is an estimate of the
value of each possible move (right, up, jump, etc.)
● The machine records its experiences at each point
in time: the screen, the action it took, the reward it
received, and the resulting screen afterwards

28. How Reinforcement Learning
Works (cont.)
● The machine then compares its prediction of the
reward it will get given a screen and given a
particular move, and compares this with the actual
result it received
● The network’s weights are adjusted to bring the two
closer
● Rinse and repeat

29. Reinforcement Learning
● Many concepts of Reinforcement Learning are
analogous to how our own minds work:
○ Learning Rate: how fast the network should
adapt to new information
○ Explore v. Exploit: how much to try new things
versus simply maxing out the best strategy
you’ve found so far

30. Reinforcement Learning
● Many concepts of Reinforcement Learning are
analogous to how our own minds work:
○ Memory Size: how long should we maintain our
memory of past experiences
○ Discount Rate: how much should we discount
future rewards over immediate rewards

31. Super Mario Bros.
● My own project was to apply Google’s methods to
play Super Mario Bros.
Here’s how it started…
And here’s how it was doing after about 72 hours...

32. The State Of The Art
● The next step for Mario - why run a single game
when you can run eight!

33. The State Of The Art
● Advances in machine learning are happening
extremely fast!
○ More powerful machines
○ The proliferation of open-source tools
○ The availability of tasks (like video games) we
can use to measure our progress

34. Where To Learn More
● Google Atari Project, and the paper in Nature
● My fork of this project to play Super Mario Bros.
● A text generator using recurrent neural nets
● The latest-and-greatest A3C algorithm for training
Atari
● The latest (free) tools of machine learning: Theano,
Torch, TensorFlow, and Chainer