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...
| Principais autores: | , , , , |
|---|---|
| Formato: | Conference item |
| Idioma: | English |
| Publicado em: |
International Conference on Learning Representations
2025
|