Entropy and outcome classification in reinforcement learning control

Several mathematical structures are proposed for learning control systems using a wide array of techniques from a variety of disciplines. Reinforcement learning offers the greatest degree of flexibility in utilizing process information in conjunction with concepts from optical control while maintaining the basic constructs used in mathematical learning theory at the direct control layer. The adaptive layer in the control hierarchy is developed based on two fundamental properties concerning the grouping of outcomes which must be satisfied if a control policy exists for the process in terms of the defined neighborhoods. The original control objective can be interpreted in light of these two objectives and a control policy will be synthesized once these conditions are satisfied by all neighborhoods constructed during the process of learning