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T. Fukao, T. Sumitomo, N. Ineyama, and N. Adachi, “Q-learning based on regularization theory to treat the continuous states and actions,” in the 1998 IEEE International Joint Conference on Neural Networks Proceedings, IEEE World Congress on Computational Intelligence, Vol. 2, pp. 1057-1062, 1998.

has been cited by the following article:

• TITLE: Control Task for Reinforcement Learning with Known Optimal Solution for Discrete and Continuous Actions

  AUTHORS: Michael C. ROTTGER, Andreas W. LIEHR

  KEYWORDS: comparison, continuous actions, example problem, reinforcement learning, performance

  JOURNAL NAME: Journal of Intelligent Learning Systems and Applications, Vol.1 No.1, July 28, 2009

  ABSTRACT: The overall research in Reinforcement Learning (RL) concentrates on discrete sets of actions, but for certain real-world problems it is important to have methods which are able to find good strategies using actions drawn from continuous sets. This paper describes a simple control task called direction finder and its known optimal solution for both discrete and continuous actions. It allows for comparison of RL solution methods based on their value functions. In order to solve the control task for continuous actions, a simple idea for generalising them by means of feature vectors is presented. The resulting algorithm is applied using different choices of feature calculations. For comparing their performance a simple measure is introduced