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| Format: | Preprint |
| Publié: |
2025
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| Accès en ligne: | https://arxiv.org/abs/2501.19027 |
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| _version_ | 1866915131078737920 |
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| author | Altahhan, Abdulrahman |
| author_facet | Altahhan, Abdulrahman |
| contents | In this paper, we develop a new planning method that extends the capabilities of the true online TD to allow an agent to efficiently replay all or part of its past experience, online in the sequence that they appear with, either in each step or sparsely according to the usual λ parameter. In this new method that we call True Online TD-Replan(λ), the λ parameter plays a new role in specifying the density of the replay process in addition to the usual role of specifying the depth of the target's updates. We demonstrate that, for problems that benefit from experience replay, our new method outperforms true online TD(λ), albeit quadratic in complexity due to its replay capabilities. In addition, we demonstrate that our method outperforms other methods with similar quadratic complexity such as Dyna Planning and TD(λ)-Replan algorithms. We test our method on two benchmarking environments, a random walk problem that uses simple binary features and a myoelectric control domain that uses both simple sEMG features and deeply extracted features to showcase its capabilities. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_19027 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | True Online TD-Replan(lambda) Achieving Planning through Replaying Altahhan, Abdulrahman Machine Learning In this paper, we develop a new planning method that extends the capabilities of the true online TD to allow an agent to efficiently replay all or part of its past experience, online in the sequence that they appear with, either in each step or sparsely according to the usual λ parameter. In this new method that we call True Online TD-Replan(λ), the λ parameter plays a new role in specifying the density of the replay process in addition to the usual role of specifying the depth of the target's updates. We demonstrate that, for problems that benefit from experience replay, our new method outperforms true online TD(λ), albeit quadratic in complexity due to its replay capabilities. In addition, we demonstrate that our method outperforms other methods with similar quadratic complexity such as Dyna Planning and TD(λ)-Replan algorithms. We test our method on two benchmarking environments, a random walk problem that uses simple binary features and a myoelectric control domain that uses both simple sEMG features and deeply extracted features to showcase its capabilities. |
| title | True Online TD-Replan(lambda) Achieving Planning through Replaying |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2501.19027 |