Class Project
Due at end of finals week
Essentially anything you want, so long
as it’s AI related and I approve
Any programming language you want
In pairs or individual
Email me by Wednesday, November 3
Projects
Implementing Knn to Classify Bedform Stability Fields
Blackjack Using Genetic Algorithms
Computer game players:Go, Checkers, Connect Four,
Chess, Poker
Computer puzzle solvers: Minesweeper, mazes
Pac

Man with intelligent monsters
Genetic algorithms:
blackjack strategy
Automated 20

questions player
Paper on planning
Neural network spam filter
Learning neural networks via GAs
Projects
Solving neural networks via backprop
Code decryptor using Gas
Box pushing agent (competing against an
opponent)
What didn’t work as well
Too complicated games:
Risk, Yahtzee, Chess, Scrabble,
Battle Simulation
Got too focused in making game work
I sometimes had trouble running the game
Game was often incomplete
Didn’t have time to do enough AI
Problems that were too vague
Simulated ant colonies / genetic algorithms
Bugs swarming for heat (emergent intelligence never happened)
Finding paths through snow
AdaBoost on protein folding data
Couldn’t get boosting working right, needed more time on
small datasets (spent lots of time parsing protein data)
Reinforcement Learning
Game playing: So far, we have
told
the
agent the value of a given board
position.
How can agent
learn
which positions
are important?
Play whole bunch of games, and receive
reward at end (+ or

)
How to determine utility of states that
aren’t ending states?
The setup: Possible game
states
Terminal states have reward
Mission: Estimate utility of all possible game
states
What is a
state
?
For chess: state is a combination of
position on board and location of
opponents
Half of your transitions are controlled by
you (your moves)
Other half of your transitions are
probabilistic (depend on opponent)
For now, we assume all moves are
probabilistic (probabilities unknown)
Passive Learning
Agent learns by “watching”
Fixed probability of moving from one state
to another
Sample Results
Technique #1: Naive Updating
Also known as Least Mean Squares (LMS)
approach
Starting at home, obtain sequence of
states to terminal state
Utility of terminal state = reward
loop back over all other states
utility for state i = running average of all
rewards seen for state i
Naive Updating Analysis
Works, but converges slowly
Must play
lots
of games
Ignores that utility of a state should
depend on successor
Technique #2: Adaptive
Dynamic Programming
Utility of a state depends entirely on the
successor state
If a state has one successor, utility should
be the same
If a state has multiple successors, utility
should be expected value of successors
Finding the utilities
To find all utilities, just solve equations
Set of linear equations, solveable
Changes each iteration as you learn
probabilities
Completely intractable for large problems:
For a real game, it means finding actual utilities of
all states
Technique 3: Temporal
Difference Learning
Want utility to depend on successors, but
want to solve iteratively
Whenever you observe a transition from i to
j:
= learning rate
difference between successive states =
temporal difference
Converges faster than Naive updating
Active Learning
Probability of going from one state to
another now depends on action
ADP equations are now:
Active Learning
Active Learning with Temporal Difference
Learning: works the same way (assuming
you know where you’re going)
Also need to learn probabilities to
eventually make decision on where to go
Exploration: where should
agent go to learn utilities?
Suppose you’re trying to learn optimal
game playing strategies
Do you follow best utility, in order to win?
Do you move around at random, hoping to
learn more (and losing lots in the process)?
Following best utility all the time can
get you stuck at an imperfect solution
Following random moves can lose a lot
Where should agent go to
learn utilities?
f(u,n) = exploration function
depends on utility of move (u), and number of
times that agent has tried it (n)
One possibility: instead of using utility to
decide where to go, use
Try a move a bunch of times, then eventually
settle
Q

learning
Alternative approach for temporal difference
learning
No need to learn probabilities: considered
more desirable sometimes
Instead, looking for “quality” of (state, action)
pair
Generalization in
Reinforcement Learning
Maintaining utilities for all seen states in a real game
is intractable.
Instead, treat it as a supervised learning problem
Training set consists of (state, utility) pairs
Or, alternatively, (state, action, q

value) triples
Learn to predict utility from state
This is a
regression
problem, not a
classification
problem
Radial basis function neural networks (hidden nodes are
Gaussians instead of sigmoids)
Support vector machines for regression
Etc…
Other applications
Applies to any situation where
something is to learn from
reinforcement
Possible examples:
Toy robot dogs
Petz
That darn paperclip
“The only winning move is not to play”
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