In information retrieval (IR), reinforcement learning (RL) is used to optimize search algorithms by treating the retrieval process as a decision-making problem. The system, or agent, interacts with the environment (user queries and responses) and receives feedback based on the quality of the retrieved documents. The goal is to maximize a reward function that measures relevance or user satisfaction.
For example, an IR system might use RL to dynamically adjust ranking functions during search to improve long-term user engagement or click-through rates. By exploring different query-document matches and observing the outcomes, the model learns the optimal strategy over time.
This approach allows IR systems to continuously improve by adapting to user behavior and preferences, leading to better personalized search results and more efficient retrieval.