A reformulation of joint chance constrained optimal control that minimizes expected cost solely over safe trajectories, solved via constrained MDP and dynamic programming with derived safety bounds under state-space gridding.
Trustworthy reinforcement learning against intrinsic vulnerabilities: Robustness, safety, and generalizability
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Agentic safety fails to generalize across tasks because the task-to-safe-controller mapping has a higher Lipschitz constant than the task-to-controller mapping alone, as proven in linear-quadratic control and demonstrated in quadcopter and LLM experiments.
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Joint Chance Constrained Safe-Optimal Control
A reformulation of joint chance constrained optimal control that minimizes expected cost solely over safe trajectories, solved via constrained MDP and dynamic programming with derived safety bounds under state-space gridding.