论文标题
珍珠：平行的进化和加固学习库
Pearl: Parallel Evolutionary and Reinforcement Learning Library
论文作者
论文摘要
强化学习越来越多地在跨领域取得成功，在这些领域中可以将问题表示为马尔可夫决策过程。进化计算算法也已证明在该领域成功，表现出与通常更复杂的增强学习的相似性能。尽管存在许多开源增强学习和进化计算库，但没有公开可用的库结合了增强比较，合作或可视化的两种方法。为此，我们创建了Pearl（https://github.com/londonnode/pearl），这是一个开源的Python库，旨在允许研究人员快速，方便地执行优化的强化学习，进化计算和两者的组合。 PEARL中的关键功能包括：模块化和可扩展的组件，自明的模块设置，张板集成，自定义回调和全面可视化。
Reinforcement learning is increasingly finding success across domains where the problem can be represented as a Markov decision process. Evolutionary computation algorithms have also proven successful in this domain, exhibiting similar performance to the generally more complex reinforcement learning. Whilst there exist many open-source reinforcement learning and evolutionary computation libraries, no publicly available library combines the two approaches for enhanced comparison, cooperation, or visualization. To this end, we have created Pearl (https://github.com/LondonNode/Pearl), an open source Python library designed to allow researchers to rapidly and conveniently perform optimized reinforcement learning, evolutionary computation and combinations of the two. The key features within Pearl include: modular and expandable components, opinionated module settings, Tensorboard integration, custom callbacks and comprehensive visualizations.
