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IEEE/ICACT20220065 Slide.13        [Big Slide]	Oral Presentation
	Next is the simulation setting. The specific scope and setting for the Observation state are shown in the table above the left. Action is divided into indexes, which are managed as action vectors. Parameters of Environment include setting the number of executions of the episode and whether to learn repeatedly one day. Parameters of an agent include epsilon, learning rate, and gamma, which are parameters of RL. It also includes rewards when the agent's action is correct and not.

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