Reinforcement Learning--Monte-Carlo
蒙特卡洛强化学习[Monte-Carlo Reinforcement Learning,MC]是强化学习中的经典方法,应用于model-free的场景中,并且可以得到相对好的结果。
蒙特卡洛强化学习[Monte-Carlo Reinforcement Learning,MC]是强化学习中的经典方法,应用于model-free的场景中,并且可以得到相对好的结果。
本年介绍了作为强化学习基础的Markov Decision Process,可以帮助理解比较重要但容易忽略的知识。
Abstract: Machine learning sits at the core of many essential products and services at Facebook. This paper describes the hardware and software infrastructure that supports machine learning at global scale. Facebook’s machine learning workloads are extremely diverse: services require many different types of models in practice. This diversity has implications at all layers in the system stack. In addition, a sizable fraction of all data stored at Facebook flows through machine learning pipelines, presenting significant challenges in delivering data to high-performance distributed training flows. Computational requirements are also intense, leveraging both GPU and CPU platforms for training and abundant CPU capacity for real-time inference. Addressing these and other emerging challenges continues to require diverse efforts that span machine learning algorithms, software, and hardware design.
BlinkDB[1] uses two key ideas: (1) an adaptive optimization framework that builds and maintains a set of multi-dimensional stratified samples from original data over time, and (2) a dynamic sample selection strategy that selects an appropriately sized sample based on a query’s accuracy or response time requirements.
Reinforcement Learning发展至今日已无法进行非常明确而清晰的分类,很多新方法也是博采众长。不过,为了更好的了解强化学习算法主要学习什么以及怎么学习,本文尝试从不同角度对主要的RL算法进行大致分类。
强化学习[Reinforcement Learning]是一个独立的机器学习研究领域,主要任务是根据不同的环境通过训练机器学习的模型来进行一系列决策。
Here are the most important software design principles discussed in this book.
This blog discuss several principles and tips about refactoring the existing code to eliminate some problems.