## 1 Introduction
There are several reasons and key factors for choosing the DDPG in this work. Firstly, wind farm control often involves making continuous adjustments to the pitch angles of wind turbine blades and other parameters. A DDPG agent is designed for solving problems with continuous action spaces, making it well-suited for such tasks. Other networks such as deep-Q networks (DQN) or artificial neural networks (ANN) are designed for discrete action spaces, and while the data can be discretized, they may be insufficient to capture all the features of the action space. DDPG also uses a deterministic policy, which can lead to more stable training and better convergence in continuous control tasks. In wind farm control, stability is crucial to ensure that the turbines operate efficiently and safely. DQN, on the other hand, uses a stochastic policy and can be less stable in continuous action spaces. Another key feature of DDPG algorithms is that they incorporate experience replay, which helps in breaking correlations between consecutive experiences, making the learning process more stable. This is important in scenarios where data can be highly correlated, such as wind data. DQN also uses experience replay, but it is more critical in continuous action spaces leading to longer training times and a higher likelihood of convergence challenges. Also, DDPG employs target networks for both the actor and critic networks. This stabilizes the learning process by making the target values less volatile. This is particularly beneficial for wind farm control, where actions can have delayed and long-term effects on the stability of the system. DQN also uses target networks, but ANNs typically do not. Another key feature of DDPG is that it utilizes an actor-critic method. This means that it maintains two separate neural networks, one for estimating the value function (critic) and one for estimating the policy (actor). This separation of tasks makes it easier to optimize the policy in complex environments such as wind farms. Wind farm control also requires a balance between exploration and exploitation. DDPG incorporates noise in the action space for exploration, which allows it to explore different control actions systematically. DDPG is known for being more sample-efficient than DQN and in wind farm control, where collecting data can be costly or time-consuming. DDPG typically requires fewer samples to achieve better performances, whereas ANNs, without a reinforcement learning framework, may struggle with the exploration-exploitation balance. Lastly, wind farm control often involves optimizing the controller for long-term rewards, such as maximizing energy production over extended periods of time. A DDPG agent is convenient for this because it considers the cumulative future rewards, while networks such as ANNs may not naturally capture this long-term perspective. DDPGs are also the perfect choice for DERs due to the decentralization of the control system, the adaptability of DDPG algorithms, the scalability of DDPG algorithms, and the highly non-linear nature of DERs and the power system. In order to see the effectiveness of the proposed DDPG controller, its performance has been compared with that of a variable proportional-integral (VPI) control-based mitigation method. Two types of cyber attacks, DoS and FDIA, have been considered in this work. The proposed technique has been simulated and validated utilizing the MATLAB/Simulink software version R2023a to demonstrate the effectiveness of the proposed method. The contributions of the paper can be summarized as follows:  
* A novel approach, i.e., a DDPG controller, is developed to mitigate cyber attacks on DERs. * The proposed method offers a low-cost approach to maintain wind farm stability during malicious cyber attacks on converter management systems. * This research analyzes the effects of different types of cyber attacks on the converter control system of a grid-connected DFIG-based wind farm. The organization of this paper is as follows. Section 2 describes the problem statement.