Many applications in multiagent learning are essentially convex optimization problems in which agents have only limited communication and partial information about the function be...
Renato L. G. Cavalcante, Alex Rogers, Nicholas R. ...
An important strength of learning classifier systems (LCSs) lies in the combination of genetic optimization techniques with gradient-based approximation techniques. The chosen app...
Martin V. Butz, Pier Luca Lanzi, Stewart W. Wilson
We present a reinforcement learning architecture, Dyna-2, that encompasses both samplebased learning and sample-based search, and that generalises across states during both learni...
work, applies a nonlinear transformation from the input space to the hidden space. The output layer Partial face images, e.g.1 eyes, nose, and ear supplies the response of the netw...
In this paper, we show how adaptive prototype optimization can be used to improve the performance of function approximation based on Kanerva Coding when solving largescale instanc...