Safe-Deep Q-Learning is a constrained deep RL algorithm that approximates Q-functions for mixed-integer wireless problems while using Lagrangian methods to enforce dual-timescale safety constraints with near-zero violation rates.
Enhancing energy efficiency in wireless-powered MEC systems through Lyapunov-guided deep reinforcement learning
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Enabling Safety-Critical Wireless Communications via Safe Reinforcement Learning
Safe-Deep Q-Learning is a constrained deep RL algorithm that approximates Q-functions for mixed-integer wireless problems while using Lagrangian methods to enforce dual-timescale safety constraints with near-zero violation rates.