| Autor: |
Chalumeau, Felix, Lim, Bryan, Boige, Raphael, Allard, Maxime, Grillotti, Luca, Flageat, Manon, Macé, Valentin, Flajolet, Arthur, Pierrot, Thomas, Cully, Antoine |
| Rok vydání: |
2023 |
| Předmět: |
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| Druh dokumentu: |
Working Paper |
| Popis: |
QDax is an open-source library with a streamlined and modular API for Quality-Diversity (QD) optimization algorithms in Jax. The library serves as a versatile tool for optimization purposes, ranging from black-box optimization to continuous control. QDax offers implementations of popular QD, Neuroevolution, and Reinforcement Learning (RL) algorithms, supported by various examples. All the implementations can be just-in-time compiled with Jax, facilitating efficient execution across multiple accelerators, including GPUs and TPUs. These implementations effectively demonstrate the framework's flexibility and user-friendliness, easing experimentation for research purposes. Furthermore, the library is thoroughly documented and tested with 95\% coverage. |
| Databáze: |
arXiv |
| Externí odkaz: |
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