On 12/19/06, Douglas S. Blank <[EMAIL PROTECTED]> wrote:
Chris,
Yes, there is the beginning of some support in Conx for similar
techniques. You can at least use this as an example. Take a look at the
SigmaNetwork in pyrobot/brain/conx.py. It is an example of a type of
CRBP (complimentary reinforcement backprop). It works like this:
[snip]
Thanks for the great example. However, is it possible for SigmaNetwork
to train multiple distinct outputs? In your example, you have 11
output nodes attempting to approximate XOR. Suppose I wanted a network
that would approximate a policy function, requiring that each output
node represent a unique action. Am I right in thinking that
SigmaNetwork would be unable to perform this task, since it requires
that all the output nodes approximate a single target?
Also, are you aware of any studies comparing CRBP to TD-Lambda? It
would be interesting to know which performs better.
Regards,
Chris
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