The authors introduce affine repulsive RL policies that provably satisfy hard affine state constraints for black-box hybrid dynamical systems with affine reset maps by deriving sufficient closed-loop safety conditions and testing on pendulum and juggler examples.
Altman,Constrained Markov Decision Processes
2 Pith papers cite this work. Polarity classification is still indexing.
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Reinforcement learning with reward machines enables sleep control in mobile networks that accounts for history-dependent, time-averaged quality of service constraints.
citing papers explorer
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Learning Control Policies to Provably Satisfy Hard Affine Constraints for Black-Box Hybrid Dynamical Systems
The authors introduce affine repulsive RL policies that provably satisfy hard affine state constraints for black-box hybrid dynamical systems with affine reset maps by deriving sufficient closed-loop safety conditions and testing on pendulum and juggler examples.
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Reinforcement Learning with Reward Machines for Sleep Control in Mobile Networks
Reinforcement learning with reward machines enables sleep control in mobile networks that accounts for history-dependent, time-averaged quality of service constraints.