Agent Reinforcement Learning by Using a Finite State Machine with Deep Neural Network
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- ZHOU Jitao
- Rikkyo University Graduate School
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- MIYAKE Youichiro
- Rikkyo University Graduate School
Bibliographic Information
- Other Title
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- ディープニューラルネットワーク付きステートマシンを用いたエージェント強化学習
Abstract
<p>An agent design by using reinforcement learning has been made progress, and there is a need for more efficient and flexible methods to control reinforcement learning. Therefore, the combination of classical decision-making model, the state machine, and deep neural network (DNN) reinforcement learning is supposed and examined. A state with DNN, in which one trained deep neural network (DNN) is installed per state, and a neural net is switched by state transition to control the movement of character. A state is a set of symbolistically defined state and connectionism NN. It makes character AI creation more flexible. In this study, a state machine with four states was built in the Unity3D environment, and the movement of a character was implemented by using reinforcement learning such that the character takes a ball in the stage right side area and transports it to the goal in the left side area. The performance, flexibility, etc. of this method are evaluated by comparing a model in which the training is split for each state of the method with a model trained by a single DNN.</p>
Journal
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- Proceedings of the Annual Conference of JSAI
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Proceedings of the Annual Conference of JSAI JSAI2022 (0), 2O5GS502-2O5GS502, 2022
The Japanese Society for Artificial Intelligence
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Details 詳細情報について
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- CRID
- 1390855656035328640
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- Text Lang
- ja
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- Data Source
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- JaLC
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- Abstract License Flag
- Disallowed