# Adaptive Clutter Intelligent Suppression Method Based on Deep Reinforcement Learning
## 4 Adaptive Clutter Intelligent Suppression Method Based on DRL
### Deep Learning Networks and Filter Parameter Decision Processes  
This article chooses the Deep Q-Network (DQN), which has stronger generalization ability and adaptability to complex environments. In the filter parameter decision model, DQN can achieve more effective suppression of clutter by learning the complex nonlinear relationship between filter parameters and environmental observations. Its representation learning ability based on deep neural networks enables agents to better understand environmental features and thus adjust filter parameters more accurately to achieve optimal signal processing results.  
DQN adopts a dual network structure consisting of an estimated value network and a target value network. The evaluation value Q network is responsible for estimating the Q value, guiding the agent to choose actions in the environment. The target value network, on the other hand, helps the training process to proceed more smoothly by providing a stable target Q value. When used together, deep Q-learning algorithms can effectively learn  
Figure 1: Diagram of the principle framework of the adaptive clutter intelligent suppression method based on DRL.  
the optimal strategy in complex environments [26]. The network parameter update process of DQN is shown in Figure 2.  
During the training process, the evaluation value Q network predicts the Q value of each possible action \(a_{t}\) based on the current input state \(s_{t}\), and these Q values represent the expected value of long-term cumulative reward obtained by taking each possible action in the current state. Then, according to the \(\varepsilon-greedy\) principle, the agent explores with a certain probability (exploration rate \(\varepsilon\)), randomly selects an action, exploits it with a probability of \(1-\varepsilon\), selects the action with the maximum Q value, and outputs \(Q(s_{t},a_{t};\theta)\). The target value network receives the next state \(s_{t+1}\) as input and predicts the \(\hat{Q}\) value of each possible action \(a^{\prime}_{t+1}\). The largest \(\hat{Q}\) value is then selected as the largest \(\hat{Q}\) value for the next state. This largest \(\hat{Q}\) value represents the maximum long-term cumulative reward that the agent can obtain in the next state, output \(\hat{Q}\big{(}s_{t+1},a^{\prime}_{t+1};\theta^{-}\big{)}\).  
In each training iteration, the evaluation value Q network updates the estimated value network parameters \(\theta\), denoted as \(L=\Big{[}r_{t}+\gamma\underset{a^{\prime}}{\text{max}}\hat{Q}\big{(}s_{t+1},a ^{\prime}_{t+1};\theta^{-}\big{)}-Q(s_{t},a_{t};\theta)\Big{]}\), by differing them from the target Q value, in order to make the predicted Q value closer to the true Q value. The parameters of the target network are the same as those of the evaluation value Q network, but the update frequency is low. By regularly updating the parameters of the target network, the parameter instability during the training process can be reduced, thus improving the stability of the training and the convergence speed.