Safe Continual Reinforcement Learning in Non-stationary Environments

Researchers tackle the intersection of safe and continual reinforcement learning, addressing a gap where RL systems must adapt to changing real-world dynamics while maintaining safety constraints throughout training and deployment. The work targets physical control systems where transient safety violations during learning are unacceptable.

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