Dr. Mark Humphrys

School of Computing. Dublin City University.

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My big idea: Ancient Brain

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CA425 - Artificial Intelligence




Notes

  1. Background

    1. Introduction to AI
    2. Survey of AI
    3. History of AI

    4. AI Links
    5. Robotics Links


  2. State-space control
    1. Continuum of Autonomy
    2. State-space control
    3. RL as Pattern Classification
    4. Reinforcement Learning - Reference


  3. Reinforcement Learning - Intro
    1. RL - The world
    2. RL - The task
    3. Exercise - long-term reward
    4. Q-learning
    5. Building up a running average
    6. How Q-learning works


  4. Movie demo
    1. Movie demo of W-learning contains within it a demo of basic Q-learning.

  5. Program code (for practical)
    1. Coding the state-space as a lookup-table
    2. Sample code for lookup-table Q-learning (Includes Boltzmann "soft max" option)

    
    
    
    [PRACTICAL CAN NOW BE LAUNCHED]
    
    
    
  6. Reinforcement Learning - More
    1. Convergence

    2. The control policy
    3. Boltzmann "soft max" distribution
    4. How to make a decision probabilistically

    5. Building a model of Pxa(r)
    6. Building a model of Pxa(y)
    7. Learning rate that does not start at 1


  7. Reinforcement Learning with Neural Networks (Pre-requisite needed.) - NOT ON COURSE THIS YEAR

    1. Neural Networks (Revision)
    2. Using a Neural Network as a generalisation in RL
    3. Q-learning with a Neural Network
    4. Using a Neural Network with RL


  8. Multiple Minds

    1. Multi-Module Reinforcement Learning
    2. Multiple Minds in the same body - Test of Hierarchical Q-learning
    3. The general form of a Society of Mind based on Reinforcement Learning
    4. Open Issues in AI
    5. Architectures of Autonomous Agents
    6. The World-Wide-Mind



Notes on Assignment Notation

I often use   :=   for assignment to distinguish from   =   for equality.

Notes on Assignment Notation



Labs

I will hold one or two labs for the practical.


Practical

Practical - Play "X's and O's" with RL




Reading

Experiments in Adaptive State-Space Robotics, Clocksin and Moore, 1989. A simple introduction to the very idea of state-space robotic or agent control.

How to Make Software Agents Do the Right Thing: An Introduction to Reinforcement Learning, Singh et al, 1996. A simple introduction to the idea of RL.

"Reinforcement Learning: A Survey", Kaelbling et al, Journal of Artificial Intelligence Research, 4:237-285, 1996. A survey.

Action Selection methods using Reinforcement Learning. My PhD thesis, 1997, has an intro to RL.


Books

Reinforcement Learning: An Introduction, Sutton and Barto, 1998. Also here.

Reinforcement Learning: State-of-the-Art, Marco Wiering and Martijn Otterlo (Editors), 2012.



Library categories



ancientbrain.com      w2mind.org      humphrysfamilytree.com

On the Internet since 1987.

Wikipedia: Sometimes I link to Wikipedia. I have written something In defence of Wikipedia. It is often a useful starting point but you cannot trust it. Linking to it is like linking to a Google search. A starting point, not a destination. I automatically highlight in red all links to Wikipedia and Google search and other possibly-unreliable user-generated content.