抄録
In a previous paper, we proposed a solution to motion planning of a mobile robot. In our approach, we formulated the problem as a discrete optimization problem at each time step. To solve the optimization problem, we used an objective function consisting of a goal term, a smoothness term and a collision term. In this paper, we propose a theoretical method using reinforcement learning for adjusting weight parameters in the objective functions. However, the conventional Q-learning method cannot be applied to a non-Markov decision process, which is caused by the smoothness term. Thus, we applied William's learning algorithm, episodic REINFORCE, to derive a learning rule for the weight parameters. This maximizes a value function stochastically. We verified the learning rule by some experiments.
本文言語 | English |
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ページ | 1-6 |
ページ数 | 6 |
出版ステータス | Published - 2001 1月 1 |
外部発表 | はい |
イベント | 2001 IEEE International Symposium on Assembly and Task Planning (ISATP2001) - Fukuoka, Japan 継続期間: 2001 5月 28 → 2001 5月 29 |
Conference
Conference | 2001 IEEE International Symposium on Assembly and Task Planning (ISATP2001) |
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国/地域 | Japan |
City | Fukuoka |
Period | 01/5/28 → 01/5/29 |
ASJC Scopus subject areas
- 工学(全般)