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.
Self- learned intelligence for integrated decision and control of automated vehicles at signalized intersections,
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
citation-role summary
background 1
citation-polarity summary
fields
cs.LG 1years
2025 1verdicts
CONDITIONAL 1roles
background 1polarities
unclear 1representative citing papers
citing papers explorer
-
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.