Decision MetaMamba: Enhancing Selective SSM in Offline RL with Heterogeneous Sequence Mixing
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arXiv
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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866910033205264384 |
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| author | Kim, Wall Song, Chaeyoung Kim, Hanul |
| author_facet | Kim, Wall Song, Chaeyoung Kim, Hanul |
| contents | Mamba-based models have drawn much attention in offline RL. However, their selective mechanism often detrimental when key steps in RL sequences are omitted. To address these issues, we propose a simple yet effective structure, called Decision MetaMamba (DMM), which replaces Mamba's token mixer with a dense layer-based sequence mixer and modifies positional structure to preserve local information. By performing sequence mixing that considers all channels simultaneously before Mamba, DMM prevents information loss due to selective scanning and residual gating. Extensive experiments demonstrate that our DMM delivers the state-of-the-art performance across diverse RL tasks. Furthermore, DMM achieves these results with a compact parameter footprint, demonstrating strong potential for real-world applications. Code is available at https://github.com/too-z/decision-metamamba |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_10517 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Decision MetaMamba: Enhancing Selective SSM in Offline RL with Heterogeneous Sequence Mixing Kim, Wall Song, Chaeyoung Kim, Hanul Machine Learning Artificial Intelligence Mamba-based models have drawn much attention in offline RL. However, their selective mechanism often detrimental when key steps in RL sequences are omitted. To address these issues, we propose a simple yet effective structure, called Decision MetaMamba (DMM), which replaces Mamba's token mixer with a dense layer-based sequence mixer and modifies positional structure to preserve local information. By performing sequence mixing that considers all channels simultaneously before Mamba, DMM prevents information loss due to selective scanning and residual gating. Extensive experiments demonstrate that our DMM delivers the state-of-the-art performance across diverse RL tasks. Furthermore, DMM achieves these results with a compact parameter footprint, demonstrating strong potential for real-world applications. Code is available at https://github.com/too-z/decision-metamamba |
| title | Decision MetaMamba: Enhancing Selective SSM in Offline RL with Heterogeneous Sequence Mixing |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2408.10517 |