Publication
Safe Continual Reinforcement Learning Methods for Nonstationary Environments. Towards a Survey of the State of the Art
Summary
Surveys safe continual online reinforcement learning under nonstationarity, organizing methods around safe learning mechanisms, adaptation to distribution shift, and safety-constraint formulation.
safe continual reinforcement learningsurveynonstationaritysafe online learningdistribution shift
Citation information
Timofey Tomashevskiy. “Safe Continual Reinforcement Learning Methods for Nonstationary Environments. Towards a Survey of the State of the Art.” 2026. arXiv:2601.05152.
The author-name form above follows the corresponding publication/preprint record. Tim Tomashevskiy also publishes under Timofey Tomashevskiy and Timothy Tomashevskiy.
Related research
This work forms part of a broader research program on adaptive AI safety, safe continual reinforcement learning, nonstationarity, and runtime assurance.