Constrained RL multiplier updates are reframed as an optimal control problem, proven equivalent to the primal problem under strong convexity, and instantiated as an MPC-based algorithm (PLO) with up to 7.2% larger feasible region than PID Lagrangian.
A transformation-aggregation framework for state representation of au- tonomous driving systems,
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Predictive Lagrangian Optimization for Constrained Reinforcement Learning
Constrained RL multiplier updates are reframed as an optimal control problem, proven equivalent to the primal problem under strong convexity, and instantiated as an MPC-based algorithm (PLO) with up to 7.2% larger feasible region than PID Lagrangian.