Reinforcement Learning Repository

Reinforcement Learning Repository is a good place for Reinforcement Learning that you may find categorized list of papers, e.g. application in robotics, hierarchical RL, use of function approximation, and … . You may find a lot of good papers about RL. It is in university of Massachusetts, Amherst. (I want to download some papers from there: remember to do so!)

Debugging tip: Human Vision Capability

I faced a troublesome problem in my programs: I have made a lot of different versions of the same program with just a little difference between. This difference can result in a complete different performance and cannot be ignored. However, I cannot remember which one is which and what are their dissimilarities. In order to find out this, it is necessary to compare two different files and to do so, I open two codes in two different windows and switch between them very fast (a few many times a second). Human vision (like most other animals including lizards (remember Jurassic park!) is very sensitive to changes, thus I can detect a single character difference between two pages very easily. Anyway, this can be applied whenever there is no tremendous difference between those two files.

C codes for Neural Network

Implementing a neural network is not very easy task. For some, it may be even impossible. This site offers C implementation of various types of neural networks including ADALINE, Multi-layer Feedforward NN (they call it Back-Propagation Network which is not correct. BP is a learning method and not an architecture), Hopfield, Self Organization Network (SOM), Adaptive Resonance Theory 1 (ART1), and a few more. I have not seen their codes, so I don’t know whether they are a good one or not- but it exists anyway (!) and may be useful someday. Thanks to Saeed for the link (he does not have a site nowadays).

Behavior Hierarchy Learning in a Behavior-based System using Reinforcement Learning (abstract)

This is my IROS 2004 paper abstract which is entitled Behavior Hierarchy Learning in a Behavior-based System using Reinforcement Learning. It will be presented very soon at International Conference on Intelligence Robots Systems [It have been presented now! You can download the paper at my publication page or directly at there].
]. I am not sure if I present it or my advisor, Dr. Nili, do so as I have some problem getting passport and VISA. Anyway, it is very interesting to go to my first international conference and visit those guys that I read their papers and adore their work, e.g. Maja Mataric of USC, Cynthia Breazeal of MIT, Leslie Pack Kaelbling of CMU, Maneula Veloso of CMU, Lynne Parker of university of Tennessee, Sridhar Mahadevan of UMass, and a lot more who I haven’t found their names yet. I wish I could visit other ones like Rodney Brooks, Marvin Minsky, Andrew Barto, Richard Sutton, Marco Dorigo, Floreano, and … and … but it seems that they do not participate in this conference. It is somehow natural as this is a Robotic conference and not all-I-lovable-topics one!!

And now, you can see the abstract. I will put the paper as soon as I upgrade my host and run an updating section for my scientific work.

Behavior Hierarchy Learning in a Behavior-based System using Reinforcement Learning
Abstract: Hand-design of an intelligent agent’s behaviors and their hierarchy is a very hard task. One of the most important steps toward creating intelligent agents is providing them with capability to learn the required behaviors and their architecture. Architecture learning in a behavior-based agent with Subsumption architecture is considered in this paper. Overall value function is decomposed into easily calculate-able parts in order to learn the behavior hierarchy. Using probabilistic formulations, two different decomposition methods are discussed: storing the estimated value of each behavior in each layer, and storing the ordering of behaviors in the architecture. Using defined decompositions, two appropriate credit assignment methods are designed. Finally, the proposed methods are tested in a multi-robot object-lifting task that results in satisfactory performance.

Paper Download

I have downloaded a lot of paper today. I wonder if I read them before the Judgment day! (I think doing so is a kind of masochism.)

Quantum Mechanics Knowledge

There’s no way to understand the interpretation of quantum mechanics without also being able to solve quantum mechanics problems – to understand the theory, you need to be able to use it (and vice versa). If you don’t heed this advice, you’ll fall prey to all sorts of nonsense that’s floating around out there.” (John BaezHow to Learn Math and Physics)

Therefore, I must confess that I do not know Quantum Mechanics (have I ever claimed so?) emmm … QM fascinates me and I would like it much if I were a physicist.