AI RESEARCH
Safe Continual Reinforcement Learning in Non-stationary Environments
arXiv CS.LG
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ArXi:2604.19737v1 Announce Type: new Reinforcement learning (RL) offers a compelling data-driven paradigm for synthesizing controllers for complex systems when accurate physical models are unavailable; however, most existing control-oriented RL methods assume stationarity and, therefore, struggle in real-world non-stationary deployments where system dynamics and operating conditions can change unexpectedly.