Three New Papers Advance Offline-to-Online Multi-Agent Reinforcement Learning Methods

Researchers publish studies on improving offline RL through online fine-tuning, generative models, and adversarial training for robot control.

Three New Papers Advance Offline-to-Online Multi-Agent Reinforcement Learning Methods

Three recent arXiv papers address different aspects of offline-to-online reinforcement learning (RL), a paradigm that uses offline data for initialization before online fine-tuning.

According to arXiv:2410.19450v2, most existing offline-to-online RL research has focused on single-agent scenarios. The paper introduces a method using “offline value function memory and sequential exploration” specifically designed for multi-agent settings.

A second paper (arXiv:2508.06269v2) presents OM2P (Offline Multi-Agent Mean-Flow Policy), which explores the integration of generative models—particularly diffusion and flow-based models—into offline multi-agent RL. According to the abstract, while these generative models show promise, “integrating powerful generative models into this framework poses unique challenges.”

The third study (arXiv:2510.13358v2) addresses robustness in robot control. According to the paper, “policies trained on static datasets remain brittle under action-space perturbations such as actuator faults.” The researchers introduce adversarial fine-tuning methods to make offline-trained policies more resilient to real-world perturbations during online deployment.

All three papers represent updated versions (v2) with cross-listing to the cs.AI category, indicating ongoing refinement of these approaches.

reinforcement learningmulti-agent systemsroboticsoffline learning