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MAGE: Multi-scale Autoregressive Generation for Offline Reinforcement Learning

arXiv:2602.23770v1 Announce Type: new Abstract: Generative models have gained significant traction in offline reinforcement learning (RL) due to their ability to model complex trajectory distributions. However, existing generation-based approaches still struggle with long-horizon tasks characterized by sparse rewards. Some hierarchical generation methods have been developed to mitigate this issue by decomposing the original problem into shorter-horizon subproblems using one policy and generating detailed actions with another. While effective, these methods often overlook the multi-scale temporal structure inherent in trajectories, resulting in suboptimal performance. To overcome these limitations, we propose MAGE, a Multi-scale Autoregressive GEneration-based offline RL method. MAGE incorporates a condition-guided multi-scale autoencoder to learn hierarchical trajectory representations, along with a multi-scale transformer that autoregressively generates trajectory representations fro

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Chenxing Lin, Xinhui Gao, Haipeng Zhang, Xinran Li, Haitao Wang, Songzhu Mei, Chenglu Wen, Weiquan Liu, Siqi Shen, Cheng Wang · · 1 min read · 12 views
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arXiv:2602.23770v1 Announce Type: new Abstract: Generative models have gained significant traction in offline reinforcement learning (RL) due to their ability to model complex trajectory distributions. However, existing generation-based approaches still struggle with long-horizon tasks characterized by sparse rewards. Some hierarchical generation methods have been developed to mitigate this issue by decomposing the original problem into shorter-horizon subproblems using one policy and generating detailed actions with another. While effective, these methods often overlook the multi-scale temporal structure inherent in trajectories, resulting in suboptimal performance. To overcome these limitations, we propose MAGE, a Multi-scale Autoregressive GEneration-based offline RL method. MAGE incorporates a condition-guided multi-scale autoencoder to learn hierarchical trajectory representations, along with a multi-scale transformer that autoregressively generates trajectory representations from coarse to fine temporal scales. MAGE effectively captures temporal dependencies of trajectories at multiple resolutions. Additionally, a condition-guided decoder is employed to exert precise control over short-term behaviors. Extensive experiments on five offline RL benchmarks against fifteen baseline algorithms show that MAGE successfully integrates multi-scale trajectory modeling with conditional guidance, generating coherent and controllable trajectories in long-horizon sparse-reward settings.

Executive Summary

The article proposes MAGE, a novel Multi-scale Autoregressive GEneration-based offline RL method, to address the limitations of existing generation-based approaches in offline reinforcement learning. MAGE incorporates a condition-guided multi-scale autoencoder and a multi-scale transformer to effectively capture temporal dependencies of trajectories at multiple resolutions. The method also employs a condition-guided decoder to exert precise control over short-term behaviors. Extensive experiments demonstrate the efficacy of MAGE in generating coherent and controllable trajectories in long-horizon sparse-reward settings. The proposed method has the potential to revolutionize offline RL by integrating multi-scale trajectory modeling with conditional guidance. The results show a significant improvement over fifteen baseline algorithms on five offline RL benchmarks.

Key Points

  • ▸ MAGE addresses the limitations of existing generation-based approaches in offline RL
  • ▸ The method incorporates a condition-guided multi-scale autoencoder and a multi-scale transformer
  • ▸ MAGE effectively captures temporal dependencies of trajectories at multiple resolutions

Merits

Strength

MAGE demonstrates significant improvement over existing methods in offline RL, showcasing its potential to revolutionize the field.

Strength

The method's ability to capture temporal dependencies at multiple resolutions enhances its ability to generate coherent and controllable trajectories.

Demerits

Limitation

The method's complexity may limit its applicability in resource-constrained environments.

Expert Commentary

The article presents a significant contribution to the field of offline reinforcement learning, addressing a critical gap in existing methods. The proposed method, MAGE, demonstrates a novel approach to integrating multi-scale trajectory modeling with conditional guidance. The results show a substantial improvement over existing methods, making MAGE a promising candidate for real-world applications. However, the method's complexity and computational requirements may limit its applicability in resource-constrained environments. Further research is needed to address these limitations and explore the potential of MAGE in various applications.

Recommendations

  • ✓ Future research should focus on developing more efficient and scalable versions of MAGE.
  • ✓ The method's applicability in various domains should be explored, including robotics and autonomous systems.

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