Reinforcement learning (computer engineering) is a paradigm within machine learning, specifically applied within the domain of computer engineering, where an intelligent agent learns to make sequential decisions by interacting with an environment to maximize a cumulative reward signal over time. This approach is fundamentally concerned with developing algorithms and systems that can learn optimal control policies through trial and error, without explicit programming of the desired behavior. In computer engineering, reinforcement learning is utilized for designing, controlling, and optimizing complex computational, robotic, and networked systems, enabling applications in areas such as robotic systems control, resource management in networking and wireless communications (including queueing systems), and the development of intelligent systems engineering solutions, often leveraging techniques from deep learning to handle high-dimensional state and action spaces.