The technology that uses reinforcement learning to optimize decision-making processes is often found in artificial intelligence applications. Reinforcement learning involves training algorithms to make sequences of decisions by receiving feedback from their actions, learning through trial and error. This approach is particularly effective for applications such as robotics, game playing, and resource management, where environments can be dynamic and complex. By exploring different strategies, the algorithm ultimately converges on optimal solutions over time, enhancing performance and efficiency in various domains, from finance to autonomous systems.