=== Published in the Journal, 		  ==== 

=== Neural Computing & Applications, 1996 ====



Neuro-Resistive Grid Approach to Trainable Controllers:

A Pole Balancing Example



Raju S. Bapi, Brendan D'Cruz, and Guido Bugmann



Abstract 

A new neural network approach is described for the task of

pole-balancing, considered a benchmark learning control problem. This

approach combines Barto, Sutton, and Anderson's [1] Associative Search

Element (ASE) with a Neuro-Resistive Grid (NRG) [2] acting as Adaptive

Critic Element (ACE). The novel feature in NRG is that it provides

evaluation of a state based on propagation of the failure information

to the neighbours in the grid. NRG is updated only on a failure and

provides ASE with a continuous internal reinforcement signal by

comparing the value of the present state to the previous state. The

resulting system learns more rapidly and with fewer computations than

that of Barto et al [1]. To establish a uniform basis of comparison of

algorithms for pole balancing, both the systems are simulated using

benchmark parameters and tests specified in [3].