Reinforcement Learning Based on Active Learning Method
  In this paper, a new reinforcement learning approach is proposed which is
based on a powerful concept named Active Learning Method (ALM) in modeling. ALM
expresses any multi-input-single-output system as a fuzzy combination of some
single-input-singleoutput systems. The proposed method is an actor-critic
system similar to Generalized Approximate Reasoning based Intelligent Control
(GARIC) structure to adapt the ALM by delayed reinforcement signals. Our system
uses Temporal Difference (TD) learning to model the behavior of useful actions
of a control system. The goodness of an action is modeled on Reward-
Penalty-Plane. IDS planes will be updated according to this plane. It is shown
that the system can learn with a predefined fuzzy system or without it (through
random actions).
