Abstract
Reinforcement learning involves learning to adapt to environments through the presentation of rewards - special input - serving as clues. To obtain quick rational policies, profit sharing, rational policy making algorithm, penalty avoiding rational policy making algorithm (PARP), PS-r* and PS-r# are used. They are called Exploitation-oriented Learning (XoL). When applying reinforcement learning to actual problems, treatment of continuous-valued input and output are sometimes required. A method based on PARP is proposed as a XoL method corresponding to the continuous-valued input, but continuous-valued output cannot be treated. We study the treatment of continuous-valued output suitable for a XoL method in which the environment includes both a reward and a penalty. We extend PARP in the continuous-valued input to continuous-valued output. We apply our proposal to the pole-cart balancing problem and confirm its validity.
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Miyazaki, K. (2010). The Penalty Avoiding Rational Policy Making Algorithm in Continuous Action Spaces. In: Fyfe, C., Tino, P., Charles, D., Garcia-Osorio, C., Yin, H. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2010. IDEAL 2010. Lecture Notes in Computer Science, vol 6283. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15381-5_22
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DOI: https://doi.org/10.1007/978-3-642-15381-5_22
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