We present an algorithm based on reinforcement and state recurrence learning techniques to solve control scheduling problems. In particular, we have devised a simple learning scheme called "handicapped learning", in which the weights of the associative search element are reinforced, either positively or negatively, such that the system is forced to move towards the desired setpoint in the shortest possible trajectory. To improve the learning rate, a variable reinforcement scheme is employed: negative reinforcement values are varied depending on whether the failure occurs in handicapped or normal mode of operation. Furthermore, to realize a simulated annealing scheme for accelerated learning, if the system visits the same failed state successively, the negative reinforcement value is increased. In examples studied, these learning schemes have demonstrated high learning rates, and therefore may prove useful for in-situ learning.

However, a large class of continuous control problems require maintaining the system at a desired operating point, or setpoint, at a given time. We refer to this problem as the basic setpoint control problem [Guha 90], and have shown that reinforcement learning can be used, not surprisingly, quite well for such control tasks.

However, a large class of continuous control problems require maintaining the system at a desired operating point, or setpoint, at a given time. We refer to this problem as the basic setpoint control problem [Guha 90], and have shown that reinforcement learning can be used, not surprisingly, quite well for such control tasks.

However, a large class of continuous control problems require maintaining the system at a desired operating point, or setpoint, at a given time. We refer to this problem as the basic setpoint control problem [Guha 90], and have shown that reinforcement learning can be used, not surprisingly, quite well for such control tasks. A more general version of the same problem requires steering the system from some 479 480 Guha initial or starting state to a desired state or setpoint at specific times without knowledge of the dynamics of the system. We therefore wish to examine how control scheduling tasks, where the system must be steered through a sequence of setpoints at specific times.