Q-Learning using Retrospective Kalman Filters
説明
Reinforcement Learning allows us to acquire knowledge without any training data. However, for learning it takes time. We discuss a case in which an agent receives a large negative reward. We assume that the reverse action allows us to improve the current situation. In this work, we propose a method to perform Reverse action by using Retrospective Kalman Filter that estimates the state one step before. We show an experience by a Hunter Prey problem. And discuss the usefulness of our proposed method.
収録刊行物
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- 2020 9th International Congress on Advanced Applied Informatics (IIAI-AAI)
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2020 9th International Congress on Advanced Applied Informatics (IIAI-AAI) 284-289, 2020-09-01
IEEE
