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IEEE/ICACT20240375 Slide.14        [Big Slide]       [YouTube]	Oral Presentation
	Three important covariates of reinforcement learning we define as follows. Action: For the optimization problem we have constructed, the action of the agent is the beamforming design of the RIS. Since the neural networks cannot handle complex numbers, we need to separate the real and imaginary parts of the RIS phases, and the action space contains 2 𝐿 elements. State: To take full advantage of the information obtained by the agent interacting with the environment, we define the state space as 𝐇 and 𝚽, the equivalent wireless channel matrix and RIS beamforming. In order to reduce the influence of the correlation of the channel state matrix, we perform a whitening operation on the channel matrix after each environmental interaction. Reward: For each episode, we will use the sum rate of the downlink transmission to determine the reward in order to choose the right action to get a higher reward for the agent.

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