# Faulty Links' Fast Recovery Method Based on Deep Reinforcement Learning
## 3 Faulty-Path Recovery Scheme Based on DDPG-LBBP  
### DDPG-LBBP Data Transmission Path Optimization Algorithm  
DDPG-LBBP combines DDPG in deep reinforcement learning with Gated Recurrent Unit (GRU) to optimize the data transmission paths by leveraging the interactive capabilities of the deep reinforcement learning environment and the decision-making capabilities of deep learning. The DDPG algorithm, based on the actor-critic framework, employs a deep neural network to adapt the policy network and the Q-network. Specifically, the actor represents the policy network and is responsible for generating action strategies, and the critic represents the Q-network and is responsible for evaluating the value of the strategy. In the DDPG algorithm, both the policy function and the value function utilize a dual neural network architecture, which includes an online network and a target network. This structure helps improve the stability of the algorithm. DDPG-LBBP incorporates the DDPG algorithm into the optimization of data transmission paths. It leverages the experience replay mechanism of DDPG to store the states, actions, rewards, and other pertinent information gathered during each time step of the agent's interaction with the environment in an experience replay pool D. When updating the parameters, the agent randomly samples from this experience replay buffer, reducing the correlation between samples and contributing to the enhancement of the algorithm's convergence and stability. DDPG-LBBP replaces the extension network in the DDPG algorithm with a GRU, integrates the GRU update process with the online network and target network in the DDPGalgorithm, and uses the update and reset mechanism of the GRU model to improve the operating efficiency of the algorithm. The DDPG- LBBP algorithm framework is shown in Figure 2.  
In Figure 2, the actor and critic networks in the DDPG-LBBP algorithm are structured as dual online and target networks with identical architectures. Notably, the GRU is employed to replace the original online policy network, target policy network, online Q-network, and target Q-network utilized in the base DDPG algorithm. This substitution aims to enhance the capabilities of the networks for sequential decision-making and long-term dependency modeling. A gate control mechanism is introduced in the GRU model, with reset gates and update gates as the core, which is conducive to timely detection of dynamic changes in the network topology, thereby more accurately adjusting the output actions of the agent. The parameter update process of the actor network and the critic network in the DDPG-LBBP algorithm is as follows:  
1. Actor network update In the DDPG-LBBP algorithm, the actor network is responsible for the output of the agent's actions. The actor network includes the online policy network and the target policy network. Among them, the online policy network is used to output the action \(a_{t}=\mu(s_{t}|\theta^{\mu})\) based on the current state \(s_{t}\) and the policy parameter \(\theta^{\mu}\). The action interacts with the environment to generate the next action. In state \(s_{t+1}\), the reward value \(r_{t}\) is obtained and the interaction information \((s_{t},a_{t},r_{t},s_{t+1})\) at that moment is stored in the experience replay pool D. The parameters of the target policy network are updated via a soft update mechanism, which is grounded in the parameters of the online policy network. To update the online policy network \(\
abla_{\theta^{\mu}}G(\mu_{\theta^{\mu}})\), the online Q-network is tasked with two primary responsibilities: providing the policy gradient \(grad[Q]\) and sampling a subset of data \((s_{t},a_{t},r_{t},s_{t+1})\) from the experience replay buffer. These sampled data are then utilized to propagate the policy gradient backward. The policy gradient is calculated in accordance with Equation (5):  
\[