arXiv:2602. 05379v2 Announce Type: replace-cross Abstract: Effective reinforcement learning (RL) for complex stochastic systems requires leveraging historical data to improve sample efficiency and accelerate policy optimization.
By Hua Zheng, Wei Xie, M. Ben Feng, Keilung Choy
arXiv:2507.06625v4 Announce Type: replace-cross
Abstract: Model Predictive Control (MPC) enables reliable trajectory optimization under dynamics constraints, but often depends on accurate dynamics mo...
By Shizhe Cai, Zeya Yin, Jayadeep Jacob, Fabio Ramos
arXiv:2607. 10369v1 Announce Type: cross Abstract: Flow-matching policies have emerged as an effective policy parameterization for robot learning.
By Rushuai Yang, Zhuo Han, Houlin Li, Hecheng Wang, Zhichao Wu, Rui Zhang, Zhaowei Zhang, Zihong Chen, Xiaohan Yan, Chiming Liu, Yi Chen, Wei Shan, Maoqing Yao
The paper introduces Solver-Gradient Guided Reinforcement Learning (SG‑RL), a method that augments standard RL with bounded gradients from a differentiable MPC solver to adapt cost‑function weights online. SG‑RL integrates solver‑gradient guidance into PPO through actor‑update scaling, policy loss, advantage estimation, and value‑function learning, achieving comparable or superior closed‑loop performance while requiring up to 70.6% fewer samples. Experiments on two autonomous racing platforms with intentional model mismatch demonstrate that SG‑RL outperforms both RL and gradient‑based policy learning baselines and generalizes zero‑shot to unseen environments.
By Baha Zarrouki, Arslan Thobani, Jasper Hoffmann, Mattia Piccinini, Rudolf Reiter, Felix Jahncke, S\'ebastien Gros, Davide Scaramuzza, Johannes Betz
Limiting‑Kernel Q(λ) (LKQL) is an off‑policy value estimator that blends n‑step truncation with a long‑horizon approximation based on the limiting kernel. It maintains the computational efficiency of n‑step methods while improving policy evaluation accuracy, especially for long‑horizon tasks. The authors prove faster convergence of LKQL’s operator under aperiodicity and near‑on‑policy conditions, and demonstrate empirical gains on MuJoCo continuous‑control benchmarks.
By Tolga Ok, Arman Sharifi Kolarijani, Peyman Mohajerin Esfahani, Mohamad Amin Sharifi Kolarijani
In value-based reinforcement learning, improving the accuracy of policy evaluation has been shown to improve downstream policy optimization performance. The widely adopted family of approximations rel...