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STAIRS-Former: Spatio-Temporal Attention with Interleaved Recursive Structure Transformer for Offline Multi-task Multi-agent Reinforcement Learning

arXiv:2603.11691v1 Announce Type: new Abstract: Offline multi-agent reinforcement learning (MARL) with multi-task datasets is challenging due to varying numbers of agents across tasks and the need to generalize to unseen scenarios. Prior works employ transformers with observation tokenization and hierarchical skill learning to address these issues. However, they underutilize the transformer attention mechanism for inter-agent coordination and rely on a single history token, which limits their ability to capture long-horizon temporal dependencies in partially observable MARL settings. In this paper, we propose STAIRS-Former, a transformer architecture augmented with spatial and temporal hierarchies that enables effective attention over critical tokens while capturing long interaction histories. We further introduce token dropout to enhance robustness and generalization across varying agent populations. Extensive experiments on diverse multi-agent benchmarks, including SMAC, SMAC-v2, MP

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Jiwon Jeon, Myungsik Cho, Youngchul Sung · · 1 min read · 16 views
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arXiv:2603.11691v1 Announce Type: new Abstract: Offline multi-agent reinforcement learning (MARL) with multi-task datasets is challenging due to varying numbers of agents across tasks and the need to generalize to unseen scenarios. Prior works employ transformers with observation tokenization and hierarchical skill learning to address these issues. However, they underutilize the transformer attention mechanism for inter-agent coordination and rely on a single history token, which limits their ability to capture long-horizon temporal dependencies in partially observable MARL settings. In this paper, we propose STAIRS-Former, a transformer architecture augmented with spatial and temporal hierarchies that enables effective attention over critical tokens while capturing long interaction histories. We further introduce token dropout to enhance robustness and generalization across varying agent populations. Extensive experiments on diverse multi-agent benchmarks, including SMAC, SMAC-v2, MPE, and MaMuJoCo, with multi-task datasets demonstrate that STAIRS-Former consistently outperforms prior methods and achieves new state-of-the-art performance.

Executive Summary

The authors propose a novel transformer architecture, STAIRS-Former, designed to address the challenges of offline multi-agent reinforcement learning with multi-task datasets. By incorporating spatio-temporal hierarchies and token dropout, STAIRS-Former enables effective attention over critical tokens and captures long interaction histories. The authors demonstrate the efficacy of STAIRS-Former through extensive experiments on diverse multi-agent benchmarks, achieving state-of-the-art performance. This architecture has the potential to revolutionize offline multi-agent reinforcement learning by improving generalization and robustness across varying agent populations.

Key Points

  • ▸ Incorporates spatio-temporal hierarchies to enable effective attention over critical tokens
  • ▸ Uses token dropout to enhance robustness and generalization
  • ▸ Achieves state-of-the-art performance on diverse multi-agent benchmarks

Merits

Strength in Hierarchical Structure

The proposed hierarchical structure allows for efficient attention over critical tokens, addressing the limitations of prior methods.

Enhanced Robustness and Generalization

The incorporation of token dropout significantly improves the robustness and generalization of STAIRS-Former across varying agent populations.

State-of-the-Art Performance

STAIRS-Former consistently outperforms prior methods on diverse multi-agent benchmarks, demonstrating its superiority in offline multi-agent reinforcement learning.

Demerits

Limited Explanation of Hyperparameter Tuning

The authors do not provide a comprehensive explanation of their hyperparameter tuning process, which may limit the replicability of their results.

Potential Overfitting to Specific Datasets

The use of token dropout may not be sufficient to prevent overfitting to specific datasets, particularly those with limited sample sizes.

Expert Commentary

The proposed STAIRS-Former architecture is a significant contribution to the field of offline multi-agent reinforcement learning. By addressing the limitations of prior methods, STAIRS-Former demonstrates improved generalization and robustness across varying agent populations. However, the authors' failure to provide a comprehensive explanation of their hyperparameter tuning process and potential overfitting concerns highlight areas for future research. Nevertheless, STAIRS-Former has the potential to revolutionize offline multi-agent reinforcement learning and has significant implications for real-world applications.

Recommendations

  • ✓ Future research should focus on providing a more comprehensive explanation of hyperparameter tuning and exploring methods to prevent overfitting.
  • ✓ The authors should conduct additional experiments to validate the robustness and generalization of STAIRS-Former across a wider range of datasets and scenarios.

Sources

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