DR-DAQP is a hybrid solver using operator splitting and active-set methods that solves affine variational inequalities exactly in finite time under specified conditions and runs up to two orders of magnitude faster than the PATH solver.
The explicit game-theoretic linear quadratic regulator for constrained multi-agent systems
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abstract
We present an efficient algorithm to compute the explicit open-loop solution to both finite and infinite-horizon dynamic games subject to state and input constraints. Our approach relies on a multiparametric affine variational inequality characterization of the open-loop Nash equilibria and extends the classical explicit constrained LQR and MPC frameworks to multi-agent non-cooperative settings. A key practical implication is that linear-quadratic game-theoretic MPC becomes viable even at very high sampling rates for multi-agent systems of moderate size. Extensive numerical experiments demonstrate order-of-magnitude improvements in online computation time and solution accuracy compared with state-of-the-art game-theoretic solvers.
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The paper provides stability criteria for multi-agent systems with heterogeneous model predictive game controllers and quantifies sensitivity of equilibria to objective misspecifications.
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\texttt{DR-DAQP}: An Hybrid Operator Splitting and Active-Set Solver for Affine Variational Inequalities
DR-DAQP is a hybrid solver using operator splitting and active-set methods that solves affine variational inequalities exactly in finite time under specified conditions and runs up to two orders of magnitude faster than the PATH solver.
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Stability and Sensitivity Analysis for Objective Misspecifications Among Model Predictive Game Controllers
The paper provides stability criteria for multi-agent systems with heterogeneous model predictive game controllers and quantifies sensitivity of equilibria to objective misspecifications.