Real-world control systems drift and change; you need to actively manage which training data you use and how confident you are in your model to handle non-stationary dynamics effectively.
This paper tackles reinforcement learning for robots and systems that change over time—like machinery that wears down or environments with shifting conditions. The researchers develop a learning algorithm that adapts by selectively forgetting old data and maintaining uncertainty estimates, proving it works better than standard approaches that assume unchanging dynamics.