Figure 8.

Typical learning curve for each task. All tasks started with random starting values for the control inputs, which were then optimized using gradient descent algorithm. One iteration of the model was said to be completed when all control inputs in the model had been optimized once. The common feature across all tasks was that almost all of the cost reduction was obtained in the first iteration with little improvement in subsequent iterations.