| Autor: |
Takase, Noriko, Kubota, Naoyuki, Baba, Norio |
| Zdroj: |
6th International Conference on Soft Computing & Intelligent Systems & The 13th International Symposium on Advanced Intelligence Systems; 2012, p1248-1252, 5p |
| Abstrakt: |
This paper deals with a state-dependent learning method of a mobile robot in dynamic and unknown environments. The aim of a mobile robot is to find the optimal path in the task of maze navigation on a grid world. Various types of reinforcement learning methods have been proposed, but it is very difficult to design the granularity (resolution) of states in search space. Therefore, we propose a multi-scale value function to enhance the initial learning of reinforcement learning. First, we compare the performance of temporal difference (TD) learning and Q-learning in dynamic environment. Here we assume several obstacles disappear in the grid world with an existence probability. Several experimental results show the effectiveness of the proposed method. [ABSTRACT FROM PUBLISHER] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
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