# A Survey: Network Feature Measurement Based on Machine Learning
## 2 Machine Learning Method in Network Measurement
### Reinforcement Learning in Network Measurement  
The challenge of agents employing learning methods to maximize gains or attain particular objectives while interacting with the environment is described and solved using one of the concepts and methodologies of reinforcement learning. The labeling of training data is not necessary for reinforcement learning, but each action environment's feedback must be either a reward or punishment. Feedback can be measured and given to the item to modify its behavior continuously. Trial and error is a hallmark of reinforcement learning, and time and delayed feedback are significant components. The data in supervised and semi-supervised learning are unrelated to one another and have no association. However, with reinforcement learning, this is not the case. The future receipt depends on the current situation and the decisions made. The two sets of data were correlated with one another. In the process of learning, known as reinforcement learning, agents regularly make decisions, monitor the outcomes, and then automatically modify their techniques to attain the best possible outcome. Even though this learning process is convergent, it still takes a long time to arrive at the best strategy because it must explore and learn about the entire system, making it unsuitable for large-scale networks. As a result, reinforcement learning's practical application is quite limited [57].  
The environment, state, action, and reward are also included in reinforcement learning, with the agent serving as its core component. One premise governs the reinforcement-learning training procedure. We think the entire procedure complies with the Markov decision-making procedure. The fundamental tenet is that the next state is only connected to the present state by the action that the present state must perform and only by one step. We can only easily deduce the next state from the current state and the action that has to be taken when it adheres to the MDP. During the training phase, it was convenient to infer the state change in each step. We cannot train if we are unable to deduce the state change at each stage of the training procedure. The three primary types of reinforcement algorithms are Value-Based, Policy-Based, and Actor-Critic.  
[PERSON] et al. [28] developed single-state Dobby bandit technology using reinforcement learning and the greedy algorithm to determine the available bandwidth. The reward function was maximized to calculate the available bandwidth. Under many challenging circumstances, it converges to the available bandwidth, has a more precise estimation and a faster convergence speed in several network scenarios, and does not require noise statistics. Utilizing the exploratory development mechanism, one can learn through untrained observation of the environment while also maximizing the cumulative return function. The method based on reinforcement learning has less variability and can also get an accurate estimate of the bandwidth available. Because we frequently run into issues when using reinforcement learning for practice training, the use of reinforcement learning in network measurement is less common. The reinforcement learning sample period is excessively long, which makes it challenging to use in practice. It is easy to fall into a locally optimal solution since incentive functions are challenging to build and balance between exploration and utilization.