Time Varying Objective

Time-varying objectives in machine learning address the challenge of adapting algorithms to situations where the goal or reward function changes over time. Current research focuses on developing algorithms robust to these changes, employing techniques like adaptive learning rates, model selection methods, and memory-enhanced architectures to handle non-stationary environments. This research is crucial for improving the performance and reliability of reinforcement learning, real-time inference systems, and other applications where dynamic environments necessitate continuous adaptation. The ultimate goal is to create algorithms that can effectively learn and perform optimally even when the desired outcome is not fixed.