In this paper, we propose a method to reduce relearning costs by applying successful policies to estimated failing states in reinforcement learning. When an environment is changed to another, relearning is needed in order to acquire an appropriate policy in the new environment. In order to reduce relearning costs, an algorithm has been proposed to estimate failing states using a decision tree C4. 5, and it relearns new policies only for the estimated failing states in the new environment. We try to reduce failing states furthermore by applying successful policies to the estimated failing states. Computer simulations show that our method can reduce relearning costs and improve the successful rate in reinforcement learning. © Springer-Verlag 2004.
CITATION STYLE
Murata, T., & Matsumoto, H. (2004). Use of successful policies to relearn for induced states of failure in reinforcement learning. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 3213, 1114–1120. https://doi.org/10.1007/978-3-540-30132-5_151
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