Learning mirror maps in policy mirror descent
Policy Mirror Descent (PMD) is a popular framework in reinforcement learning, serving as a unifying perspective that encompasses numerous algorithms. These algorithms are derived through the selection of a mirror map and enjoy finite-time convergence guarantees. Despite its popularity, the explorati...
| 主要な著者: | , , , , |
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| フォーマット: | Conference item |
| 言語: | English |
| 出版事項: |
International Conference on Learning Representations
2025
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