Accelerating Reinforcement Learning via MPC Solver-Gradient Guidance for Weights-varying MPC
In Model Predictive Control (MPC), cost-function weights shape closed-loop behavior, yet changing conditions often make fixed parametrizations suboptimal and motivate context-dependent online adaptation. Learning such policies is difficult because behavior depends implicitly on numerical MPC solutions, producing nonlinear, potentially nonsmooth, long-horizon dependencies on policy parameters. This creates a bias-variance tradeoff: Reinforcement Learning (RL) optimizes realized closed-loop return from environment samples but is sample-inefficient, whereas Gradient-Based Policy Learning (GB-PL) uses low-variance solver gradients from differentiable MPC to optimize surrogate losses on predic...
| Authors | Baha Zarrouki, Arslan Thobani, Jasper Hoffmann et al. |
|---|---|
| Primary category | cs.RO |
| Published | 1 September 2026 |
| Last updated at source | 1 September 2026 |
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