  A new training algorithm is presented for delayed reinforcement learning
problems that does not assume the existence of a critic model and employs the
polytope optimization algorithm to adjust the weights of the action network so
that a simple direct measure of the training performance is maximized.
Experimental results from the application of the method to the pole balancing
problem indicate improved training performance compared with critic-based and
genetic reinforcement approaches.

Neural and Evolutionary Computing