Finite Time

Finite-time analysis in various fields, including reinforcement learning and control systems, focuses on establishing rigorous bounds on the convergence speed and sample complexity of algorithms. Current research emphasizes developing finite-time guarantees for algorithms employing neural networks, linear function approximation, and asynchronous updates, often within the context of multi-agent systems and Markov decision processes. These analyses are crucial for understanding the practical performance and scalability of these algorithms, impacting the design and deployment of efficient and reliable systems in diverse applications such as robotics, quantum computing, and supply chain optimization. The overarching goal is to move beyond asymptotic analyses and provide concrete performance guarantees for real-world applications.