Data Valuation using Reinforcement Learning (DVRL) utilizes Reinforcement Learning, a subfield of machine learning where an agent learns by interacting with an environment over time. At each step, the agent chooses an action to interact with the environment and is given feedback, either a reward if the action was favorable towards the agent's goal or a punishment otherwise. DVRL uses this general framework to compute influence values for the training data. The agent, here the data value estimator, is fed with a set of training samples and assigns each training sample a sampling probability. The higher a training sample's assigned value, the more likely it will be sampled and fed to the environment, in this case, the predictor. The predictor is trained on the sampled training instances and evaluated on a separate validation set. The reward function then sends a reward or punishment to the agent, dependent on the performance change compared to previous iterations. Over time, the data value estimator learns which samples will result in the maximum reward, assigning them the highest values. These optimal values can be interpreted as the importance scores. Their interpretation is analogous to the previous methods.