Solving Multi-objective Reinforcement Learning Problems by EDA-RL - Acquisition of Various Strategies

  • Authors:

  • Hisashi Handa

  • Affiliations:

  • -

  • Venue:
  • ISDA '09 Proceedings of the 2009 Ninth International Conference on Intelligent Systems Design and Applications
  • Year:
  • 2009

Quantified Score

Hi-index 0.00

Visualization

Abstract

EDA-RL, Estimation of Distribution Algorithms for Reinforcement Learning Problems, have been proposed by us recently. The EDA-RL can improve policies by EDA scheme: First, select better episodes. Secondly, estimate probabilistic models, i.e., policies, and finally, interact with the environment for generating new episodes. In this paper, the EDA-RL is extended for Multi-Objective Reinforcement Learning Problems, where reward is given by several criteria. By incorporating the notions in Evolutionary Multi-Objective Optimization, the proposed method is enable to acquire various strategies by a single run.