arXiv AI
Oct 9

Higher-Order Action Supervision Makes A Strong Policy Class

The paper proposes a new loss scheme that simultaneously supervises zeroth- and first-order actions to improve policy performance and control robustness in data-driven decision-making methods such as imitation learning and reinforcement learning. The approach provides formal theoretical guarantees, works with any off-the-shelf policy model, and can be added as a lightweight plug‑and‑play module to existing offline RL frameworks. Experiments on OGBench and D4RL show significant gains in performance, robustness, and out‑of‑distribution generalization, especially in low‑data settings.

By Peng Cheng, Yunxian Hou, Zhi Zhou, Qian Zhang, Chang Huang, Xianyuan Zhan
arXiv Machine Learning
Jun 2

All Models are Wrong, Knowing Where is Useful: On Model Uncertainty in Reinforcement Learning

arXiv:2606. 01363v1 Announce Type: new Abstract: Model-based reinforcement learning (MBRL) infers information about the environment from a learned dynamics model and bears the potential to address open problems such as data efficient and safe learning in robotics.

By Bernd Frauenknecht, Devdutt Subhasish, Artur Eisele, Friedrich Solowjow, Sebastian Trimpe