State Adaptive

State-adaptive systems dynamically adjust their behavior or parameters based on the current state or context, aiming to optimize performance in diverse and changing environments. Current research focuses on developing algorithms that learn these adaptive strategies, employing techniques like reinforcement learning with state-dependent balance coefficients and large language models for improved generalization across varied datasets. This approach is proving valuable in diverse applications, including enhancing battery health estimation, improving the efficiency of reinforcement learning algorithms, and optimizing resource allocation in delay-sensitive applications like telemedicine.