Aspects of learning neural control

The possibilities and restrictions for applying neural architectures to control problems are discussed in the light of available a priori knowledge. Reinforcement learning is shown to provide a useful solution in the absence of any a priori knowledge. Two reinforcement learning architectures are proposed and the quality of the incrementally acquired control strategy is discussed on the well known cart pole benchmark problem