Multi-Resolution State-Space Discretization for Q-Learning with Pseudo-Randomized Discretization uri icon

abstract

  • A multiresolution state-space discretization method with pseudorandom gridding is developed for the episodic unsupervised learning method of Q-learning. It is used as the learning agent for closed-loop control of morphing or highly reconfigurable systems. This paper develops a method whereby a state-space is adaptively discretized by progressively finer pseudorandom grids around the regions of interest within the state or learning space in an effort to break the Curse of Dimensionality. Utility of the method is demonstrated with application to the problem of a morphing airfoil, which is simulated by a computationally intensive computational fluid dynamics model. By setting the multiresolution method to define the region of interest by the goal the agent seeks, it is shown that this method with the pseudorandom grid can learn a specific goal within 0.001 while reducing the total number of state-action pairs needed to achieve this level of specificity to less than 3000. 2011 South China University of Technology, Academy of Mathematics and Systems Science, Chinese Academy of Sciences and Springer-Verlag Berlin Heidelberg.

published proceedings

  • 2010 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS IJCNN 2010

author list (cited authors)

  • Lampton, A., Valasek, J., & Kumar, M.

citation count

  • 2

complete list of authors

  • Lampton, Amanda||Valasek, John||Kumar, Mrinal

publication date

  • August 2010