Reinforcement learning and behavior optimization are becoming more and more popular in the field of robotics because algorithms are mature enough to tackle real problems in this domain. Robust implementations of state-of-the-art algorithms are often not publicly available though, and experiments are hardly reproducible because open-source implementations are often not available or are still in a stage of research code. Consequently, often it is infeasible to deploy these algorithms on robotic systems. BOLeRo closes this gap for policy search and evolutionary algorithms by delivering open-source implementations of behavior learning algorithms for robots. It is easy to integrate in robotic middlewares and it can be used to compare methods and develop prototypes in simulation.

中文翻译：

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BOLeRo：机器人的行为优化和学习

强化学习和行为优化在机器人领域变得越来越流行，因为算法已经足够成熟，可以解决该领域的实际问题。然而，最先进算法的稳健实现通常不公开可用，并且实验几乎不可重复，因为开源实现通常不可用或仍处于研究代码阶段。因此，在机器人系统上部署这些算法通常是不可行的。BOLeRo 通过为机器人提供行为学习算法的开源实现，缩小了策略搜索和进化算法的这一差距。它很容易集成到机器人中间件中，可用于比较方法和开发模拟原型。