Secure planning against stealthy attacks on robot actuators in unknown stochastic environments is achieved by modeling the problem as a stochastic game and solving it with model-free RL to satisfy a combined LTL formula.
Network scheduling for secure cyber-physical systems
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Secure Planning Against Stealthy Attacks via Model-Free Reinforcement Learning
Secure planning against stealthy attacks on robot actuators in unknown stochastic environments is achieved by modeling the problem as a stochastic game and solving it with model-free RL to satisfy a combined LTL formula.