Symbolic structures in numeric reinforcement for learning optimum robot trajectory ?
This article contributes to the understanding of how symbols can profitably be integrated into a numeric reinforcement learning approach so as to realize a learning robot in a continuous problem setting. An initial experiment clearly revealed that the underlying task could not be learned merely on the basis of numeric reinforcement values. The unstructured utilization of sensory values of the reinforcement approach is dealt with by incorporating readily available domain knowledge. The domain knowledge pre-labels the task space into different regions and builds a connection between the direct sensory experience and the high level features. Teams of networks, each dedicated to a particular feature, exploit the differential characteristics of the regions to rapidly learn the sub-tasks. In addition, a pre-wired fuzzy controller whose function is to restrict the set of actions from which the learner composes a control policy is integrated into the learner. The task faced by the robot has interesting characteristics that are unknown initially, and must be learned. Experimental results indicate that after the robot has been placed in the task space and given the externally motivated needs, it has been able to learn, with a handful of trials, the right policy that fulfills the external needs.
numeric reinforcement domain knowledge radial basis functions fuzzy behavior