Higher-Order Action Supervision Makes A Strong Policy Class

2026-10-07

AI Summary

This study addresses the instability and poor robustness in robot control arising from imitation and reinforcement learning methods that supervise only zeroth-order actions. To overcome this limitation, we propose a plug-and-play higher-order action supervision loss that requires no architectural modifications to existing models. By jointly constraining zeroth- and first-order actions, this approach enhances policy performance and control stability with theoretical guarantees, while remaining compatible with deterministic, stochastic, and flow-based policy models. Integrated within an offline reinforcement learning framework and combined with model ensembling techniques, our method achieves significant improvements on the OGBench and D4RL benchmarks. Specifically, it demonstrates superior performance, enhanced robustness, and improved out-of-distribution generalization in low-data regimes for continuous control tasks.

Abstract

Modern data-driven decision-making methods, such as imitation learning (IL) and reinforcement learning (RL), have achieved great success in solving many complex tasks. However, these methods often suffer from serious control instability and robustness issues when applied in real-world applications such as robotics and autonomous driving, posing notable challenges for their practical deployment. We argue that this instability issue stems largely from their limitations in solely supervising and optimizing zeroth-order actions (i.e., the action labels), failing to account for higher-order action dynamics and temporal consistency. In this paper, we show that simultaneously supervising both zeroth- and first-order actions can dramatically enhance policies' performance and control robustness. To achieve this, we introduce a novel and elegant loss scheme supported by formal theoretical guarantees that can equip any off-the-shelf policy model (e.g., deterministic, stochastic, or flow policies) with the capability for higher-order action supervision, without requiring any structural modifications. Moreover, our proposed method can serve as a lightweight plug-and-play module that seamlessly integrates with a broad spectrum of existing offline RL frameworks. Extensive evaluations on OGBench and D4RL demonstrate that our approach yields substantial performance and robustness improvements across a wide range of continuous control environments. Notably, our method can also enhance policies' out-of-distribution (OOD) generalization capability in the challenging low-data regime, making it an ideal tool in tackling many real-world control problems.

Problem

• imitation learning
• reinforcement learning
• control instability
• robustness
• higher-order action supervision

Innovation

• Higher-Order Action Supervision
• Offline Reinforcement Learning
• Control Robustness
• Plug-and-Play Module
• Out-of-Distribution Generalization