What Is the Role of Machine Learning in Databases?

Zongheng Yang blog, Database Systems, Deep Learning, Systems 0 Comments

(This article was authored by Sanjay Krishnan, Zongheng Yang, Joe Hellerstein, and Ion Stoica.) What is the role of machine learning in the design and implementation of a modern database system? This question has sparked considerable recent introspection in the data management community, and the epicenter of this debate is the core database problem of query optimization, where the database system finds the best physical execution path for an SQL query. The au courant research direction, inspired by trends in Computer Vision, Natural Language Processing, and Robotics, is to apply deep learning; let the database learn the value of each execution strategy by executing different query plans repeatedly (an homage to Google’s robot “arm farm”) rather through a pre-programmed analytical …

An Open Source Tool for Scaling Multi-Agent Reinforcement Learning

Eric Liang blog, Distributed Systems, Ray, Reinforcement Learning 0 Comments

We just rolled out general support for multi-agent reinforcement learning in Ray RLlib 0.6.0. This blog post is a brief tutorial on multi-agent RL and how we designed for it in RLlib. Our goal is to enable multi-agent RL across a range of use cases, from leveraging existing single-agent algorithms to training with custom algorithms at large scale.

RLlib: Abstractions for Distributed Reinforcement Learning

Eric Liang

Reinforcement learning (RL) algorithms involve the deep nesting of highly irregular computation patterns, each of which typically exhibits opportunities for distributed computation. We argue for distributing RL components in a composable way by adapting algorithms for top-down hierarchical control, thereby encapsulating parallelism and resource requirements within short-running compute tasks. We demonstrate the benefits of this principle through RLlib: a library that provides scalable software primitives for RL. These primitives enable a broad range of algorithms to be implemented with high performance, scalability, and substantial code reuse. RLlib is available at https://github.com/ray-project/ray.

Authors: Eric Liang, Richard Liaw, Robert Nishihara, Philipp Moritz, Roy Fox, Kenneth Goldberg, Joseph Gonzalez, Michael Jordan, Ion Stoica