QDax: A Library for Quality-Diversity and Population-based Algorithms with Hardware Acceleration
원문 초록
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.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
Sparse-Reg: Improving Sample Complexity in Offline Reinforcement Learning using Sparsity
DR-SAC: Distributionally Robust Soft Actor-Critic for Reinforcement Learning under Uncertainty
CO-PILOT: COllaborative Planning and reInforcement Learning On sub-Task curriculum
Supervised Fine Tuning on Curated Data is Reinforcement Learning (and can be improved)
Advancements and Challenges in Continual Reinforcement Learning: A Comprehensive Review
Scaling Algorithm Distillation for Continuous Control with Mamba
토론
이 논문과 연결된 포럼 글이 아직 없습니다.