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- W4384303204 abstract "Most deep reinforcement learning methods focus on the agent’s learning performance but ignore the uncertainty in the environment, which would lead to a lot of redundant interaction data and slow learning speed. We propose two novel policy frameworks that extract human knowledge through fuzzy inference systems and incorporate it into deep reinforcement learning to accelerate agent training. We also compared agent learning for two different combinations. Our frameworks allow the agent to choose an action based on a new probability distribution that reduce the negative impact of future events. Thereby helping the agent reduce invalid interaction data and improve learning efficiency. We conduct experiments in multiple game environments, and the result exhibits that our method can effectively reduces training episodes, and achieve a better learning result in the mid-term." @default.
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- W4384303204 date "2023-01-01" @default.
- W4384303204 modified "2023-09-27" @default.
- W4384303204 title "Accelerating Deep Reinforcement Learning with Fuzzy Logic Rules" @default.
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- W4384303204 doi "https://doi.org/10.1007/978-3-031-36822-6_23" @default.
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