  This paper introduces the NK Echo State Network. The problem of learning in
the NK Echo State Network is reduced to the problem of optimizing a special
form of a Spin Glass Problem known as an NK Landscape. No weight adjustment is
used; all learning is accomplished by spinning up (turning on) or spinning down
(turning off) neurons in order to find a combination of neurons that work
together to achieve the desired computation. For special types of NK
Landscapes, an exact global solution can be obtained in polynomial time using
dynamic programming. The NK Echo State Network is applied to a reinforcement
learning problem requiring a recurrent network: balancing two poles on a cart
given no velocity information. Empirical results shows that the NK Echo State
Network learns very rapidly and yields very good generalization.
