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IEEE/ICACT20240375 Slide.15        [Big Slide]       [YouTube]	Oral Presentation
	For the non-episode task, we can use average reward instead of the instantaneous reward to allow the agent to learn from the environment as the formula. The agents to enter the state-action space where Q prediction is difficult to perform, thus allowing them to correct the prediction error of the unknown or less-selected actions as this formula, where ¢Á is the Hadamard product and the 𝜆¡ô(0,1] is used to restrict the explorer network from influencing the policy network to choose the wrong action.

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