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Modeling of autonomous problem solving process by dynamic construction of task models in multiple tasks environment
Title: | Modeling of autonomous problem solving process by dynamic construction of task models in multiple tasks environment |
Authors: | Ohigashi, Yu Browse this author | Omori, Takashi Browse this author |
Keywords: | Model-based reinforcement learning | Multiple tasks | Task model | Reuse of knowledge |
Issue Date: | Oct-2006 |
Journal Title: | Neural Networks |
Volume: | 19 |
Issue: | 8 |
Start Page: | 1169 |
End Page: | 1180 |
Publisher DOI: | 10.1016/j.neunet.2006.05.037 |
PMID: | 16989982 |
Abstract: | Traditional reinforcement learning (RL) supposes a complex but single task to be solved. When a RL agent faces a task similar to a learned one, the agent must relearn the task from the beginning because it doesn’t reuse the past learned results. This is the problem of quick action learning, which is the foundation of decision making in the real world. In this paper, we suppose agents that can solve a set of tasks similar to each other in a multiple tasks environment, where we encounter various problems one after another, and propose a technique of action learning that can quickly solve similar tasks by reusing previously learned knowledge. In our method, a model-based RL uses a task model constructed by combining primitive local predictors for predicting task and environmental dynamics. To evaluate the proposed method, we performed a computer simulation using a simple ping-pong game with variations. |
Relation: | http://www.sciencedirect.com/science/journal/08936080 |
Type: | article (author version) |
URI: | http://hdl.handle.net/2115/16898 |
Appears in Collections: | 情報科学院・情報科学研究院 (Graduate School of Information Science and Technology / Faculty of Information Science and Technology) > 雑誌発表論文等 (Peer-reviewed Journal Articles, etc)
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Submitter: 大東 優
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