- Don’t think this was peer reviewed, but was cited in Kaelbling’s RL survey
- Basic idea seems similar to CACLA (although this came first – 1993), uses wire-fitting and FAs
- Wire fitting works by having a discrete number of “wires” that represent control points on the function, areas between control points are estimated by interpolation between them.
- Maxima must be at the wires (I think?)

- Wire control points are initialized randomly and moved around by training
- Say that it makes more sense with a gradient-based (I guess online updates) system, but can also be adapted to a memory based system
- Still dont really understand how the wire fitting is done – the interpolation I get – I can never find a good reference on the algorithm itself, and I haven’t seen it explained very well
- “Any general FA system can be used to learn the function… This function generates a set of control points based upon the the value of x. A function is then fitted to the set of control points, and the value of
*f*(.) is then calculated from*u*(the actions). “

- “Any general FA system can be used to learn the function… This function generates a set of control points based upon the the value of x. A function is then fitted to the set of control points, and the value of
- So you really need an FA that has two outputs for each wire, so if there are
*w*wires, the FA has an output vector of size 2*w*for a query*x*. I think this is whats doing most of the work, and don’t get how the location of the wires is selected

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