| Autor: |
Gyeongmin Kim, Jeonghye Kim, Suyoung Lee, Jaewoo Baek, Howon Moon, Sangheon Shin, Youngchul Sung |
| Jazyk: |
angličtina |
| Rok vydání: |
2024 |
| Předmět: |
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| Zdroj: |
IEEE Access, Vol 12, Pp 135283-135299 (2024) |
| Druh dokumentu: |
article |
| ISSN: |
2169-3536 |
| DOI: |
10.1109/ACCESS.2024.3462803 |
| Popis: |
Recent advancements in offline reinforcement learning (RL) have showcased the potential for leveraging static datasets to train optimal policies. However, real-world applications often face challenges due to missing or incomplete state information caused by imperfect sensor performance or intentional interlaces. We propose the Dimension-Wise Drop Decision Transformer (D3T), a novel framework designed to address dimension-wise data loss in sensor observations, enhancing the robustness of RL algorithms in real-world scenarios. D3T innovatively incorporates dimension-wise drop information embeddings within the Transformer architecture, facilitating effective decision-making even with incomplete observations. Our evaluation in the D4RL MuJoCo domain demonstrates that D3T significantly outperforms existing methods such as the Decision Transformer, particularly with substantial dimension-wise drops of observations. These results confirm D3T’s capability in managing real-world imperfections in state observations and illustrate its potential to substantially expand the applicability of RL in more complex and dynamic environments. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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