Reference simulator and stored results for Lyapunov-guided safe RL with risk-budget feasibility in energy-aware O-RAN scheduling: CVaR tail constraint, LCB safety filter, PID dual, and a swept classical drift-plus-penalty frontier.
reinforcement-learning reproducible-research resource-allocation energy-efficiency actor-critic 5g ppo safe-reinforcement-learning wireless-networks cvar o-ran open-ran lyapunov-optimization constrained-mdp risk-sensitive-rl drift-plus-penalty network-energy-saving
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Updated
Aug 23, 2026 - Python