Recurrent neural-network training by a learning automaton approach for trajectory learning and control system design

We present a training approach using concepts from the theory of stochastic learning automata that eliminates the need for computation of gradients. This approach also offers the flexibility of tailoring a number of specific training algorithms based on the selection of linear and nonlinear reinforcement rules for updating automaton action probabilities. The training efficiency is demonstrated by application to two complex temporal learning scenarios, viz, learning of time-dependent continuous trajectories and feedback controller designs for continuous dynamical plants. For the first problem, it is shown that training algorithms can be tailored following the present approach for a recurrent neural net to learn to generate a benchmark circular trajectory more accurately than possible with existing gradient-based training procedures. For the second problem, it is shown that recurrent neural-network-based feedback controllers can be trained for different control objectives.