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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

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class project
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
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
projects1
Projects
  • Solving neural networks via backprop
  • Code decryptor using Gas
  • Box pushing agent (competing against an opponent)
what didn t work as well
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
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
The setup: Possible game states
  • Terminal states have reward
  • Mission: Estimate utility of all possible game states
what is a state
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
Passive Learning
  • Agent learns by “watching”
  • Fixed probability of moving from one state to another
technique 1 naive updating
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
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
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
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
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:
  • a = learning rate
  • difference between successive states = temporal difference
  • Converges faster than Naive updating
active learning
Active Learning
  • Probability of going from one state to another now depends on action
  • ADP equations are now:
active learning1
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
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
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
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
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
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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