Reinforcement Learning in Time-Varying Systems: an Empirical Study. (arXiv:2201.05560v1 [cs.LG])

Recent research has turned to Reinforcement Learning (RL) to solve
challenging decision problems, as an alternative to hand-tuned heuristics. RL
can learn good policies without the need for modeling the environment's
dynamics. Despite this promise, RL remains an impractical solution for many
real-world systems problems. A particularly challenging case occurs when the
environment changes over time, i.e. it exhibits non-stationarity. In this work,
we characterize the challenges introduced by non-stationarity and develop a
framework for addressing them to train RL …

arxiv learning reinforcement learning study systems time