The Reinforcement Learning (RL) Open Source Fest is a global online program focused on introducing students to open source reinforcement learning programs and software development while working alongside researchers, data scientists, and engineers on the Real World Reinforcement Learning team at Microsoft Research NYC. Students will work on a four-month research programming project during their break from university (May-August 2020). Accepted students will receive a $10,000 USD stipend.
Our goal is to bring together a diverse group of students from around the world to collectively solve open source reinforcement learning problems and advance the state-of-the-art research and development alongside the RL community while providing open source code written and released to benefit all.
At the end of the program, students will present each of their projects to the Microsoft Research Real World Reinforcement Learning team online. Three students and their projects will be selected as the finalists of the RL Open Source Fest and be offered the opportunity to present their projects in-person at the Microsoft Research lab in NYC (travel and accommodation covered by Microsoft).
Open source problems
Vowpal Wabbit (VW) is an open source machine learning library created by John Langford and developed by Microsoft Research with the help of many contributors. It is a fast, flexible, online, and active learning solution that empowers people to solve complex interactive machine learning problems, with a large focus on contextual bandits and reinforcement learning. It is a vehicle for both research prototyping and driving bleeding edge algorithms to production. RL OS Fest is all about open source problems in the Vowpal Wabbit ecosystem.
Check out the list of open source problems here.
To be eligible for the program, students must be enrolled in or accepted into an accredited institution including colleges, universities, Master programs, PhD programs, and undergraduate programs.
Student responsibilities during the program
- Submit quality work: code compiles, has unit tests and documentation, and passes code review
- Regularly communicate work completed, what you intend to do next, and blockers
- Re-evaluate project tasks if you’re significantly ahead or behind schedule
- Regular check-ins with your mentor/collaborator
- Listen and respond to feedback
- Pro-active learning
What makes a successful project?
Success looks different for every project. Challenging yourself and developing skills and knowledge are the most important part. Producing some sort of deliverable item is great, but not strictly required. We all know how development and experimentation goes, unforeseen problems can come up and present new challenges and that’s all part of the process. You’ll have a mentor and support along the way.
- A successful engineering-oriented project might include pull requests merging your work, a design document, tests, and general documentation
- A successful data science-oriented project might involve pull requests, reproducible experiments, data-sets, a report, and visualized results
- A successful prototyping-oriented project might include an MVP, tests, and documentation
Evaluation criteria for project finalists
- Finished goals from the project
- Overall code quality
- Good documentation