# A Deep Evolution Policy-Based Approach for RIS-Enhanced Communication System
The key insight is that model sparsification via weight pruning can significantly reduce the gap between exact unlearning and approximate unlearning. Different from DL, deep reinforcement Learning (DRL) is an online training algorithm that embraces the advantage of DL in neural network training as well as improving the learning speed and performance of reinforcement learning (RL) algorithms. A standard DRL optimization framework is established in [20], which is based on an RIS-assisted MU-MISO communication system. This framework allows for the simultaneous optimization of both active and passive beamforming with minimal implementation complexity. In [21], a DRL algorithm is proposed to jointly optimize beamforming by developing a position-sensing model that reduces interactive overhead. In [22], an RIS-aided downlink orthogonal frequency division multiplexing (OFDM) transmission system is investigated, which employs the twin delayed deep deterministic policy gradient (TD3) method for optimizing RIS phase shifts to minimize the transmission power. In [23], the authors introduce a Soft Actor-Critic (SAC) algorithm aimed at jointly optimizing transmit beamforming and RIS phase shifts within a MU-MISO system, with the objective of maximizing the sum downlink rate under a phase-dependent reflection amplitude model. Ref. [24] explores a downlink OFDM transmission system aided by RIS, proposing a DRL-based optimization algorithm that utilizes a deep Q-network (DQN) framework to enhance spectral efficiency. In [25], a novel approach derived from the deep deterministic policy gradient (DDPG) algorithm, specifically the Shared Deep Deterministic Policy Gradient (SD-DDPG) algorithm, is introduced to address the power allocation and trajectory optimization challenges for downlink unmanned aerial vehicle (UAV)-mobile instruments (MIs). In [26], the simultaneous transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) assisted non-orthogonal multiple access (NOMA) system is investigated, which includes a cooperative jammer and dual eavesdroppers. The DDPG framework is employed to maximize the sum secrecy rate by jointly optimizing channel allocation, transmit power, and coefficient matrices under both perfect and imperfect CSI. However, gradient descent-based AI algorithms, such as DL and DRL, require the minimization of a loss function, necessitating that the optimization model be differentiable. Additionally, the development of an agent with appropriate hyperparameters can pose significant challenges. In recent years, metaheuristic algorithms have been employed to address complex real-world problems. The challenges that emerge from various domains, including economics, engineering, politics, and management, are significant [27; 28; 29]. In [30], an innovative genetic locus selection scheme is introduced, which serves as the basis for parameter configuration of the crossover operator by formulating the process as a Markov Decision Process (MDP). Ref. [31] examines an uplink RIS-aided massive MIMO system, focusing on the optimization of phase shifts at the RIS through a genetic algorithm (GA) to maximize both the sum rate and the minimum user rate. Furthermore, ref. [32] explores the performance of RIS-aided massive MIMO systems that incorporate direct links, with the phase shifts of the RIS being designed via GA based on statistical CSI. The aforementioned works primarily integrate evolution learning (EL) and RL, wherein individuals are represented by discrete vectors. However, the use of parameter vectors may limit the expression of complex features, potentially resulting in a reduction in diversity within the population. Inspired by the benefits of EL and DRL, we propose an algorithm based on deep evolution policy (DEP).