2020 AI@Unity interns shoutout

Each summer, interns join AI@Unity to develop highly impactful technology that forwards our mission to empower Unity developers with Artificial Intelligence and Machine Learning tools and services. This past summer was no different, and the AI@Unity group was delighted to have 24 fantastic interns. This post will highlight the seven research and engineering interns from the ML-Agents and Game Simulation teams: Yanchao Sun, Scott Jordan, PSankalp Patro, Aryan Mann, Christina Guan, Emma Park and Chingiz Mardanov. Read on to find out more about their experiences and achievements interning at Unity.

During the summer of 2020, we had a total of 24 interns in the AI@Unity organization, seven of whose projects will be overviewed here. What was particularly remarkable is that all seven projects were experimental in nature which helped us push the boundaries of our products and services. All seven projects listed below will eventually make their way back into the core product in the coming months as key features that will delight our users.

The seven interns whose projects are overviewed in this blog post were part of the ML-Agents and Game Simulation teams:

The ML-Agents team is an applied research team and developers and maintains the  ML-Agents Toolkit, an open-source project. The ML-Agents Toolkit enables Unity games and simulations to serve as training environments for machine learning algorithms. Developers use ML-Agents to train character behaviors or game AIs with deep reinforcement learning (RL) or imitation learning (IL). This avoids the tediousness of traditional hand-crafted or hard-coded methods. Aside from the GitHub documentation, you can learn more about ML-Agents in this blog post and research paper. The Game Simulation team is a product team whose mission is to enable game developers to test and balance their game by running multiple playthroughs in parallel in the cloud. Game Simulation launched earlier this year, and you can learn more by checking out the case studies we published with our partners iLLOGIKA and Furyion.

As Unity grows, our internship program grows as well. In 2021, the size of the AI@Unity internship program will increase to 28 positions. Additionally, we are hiring in more locations, including Orlando, San Francisco, Copenhagen, Vancouver, and Vilnius, for positions ranging from software development to machine learning research. If you are interested in our 2021 internship program, please apply here (and watch this link as we’ll post additional internship roles in the coming weeks). And now, please enjoy the many and varied projects of our talented interns from summer 2020!

Yanchao Sun (ML-Agents): Transfer learning

In most cases, a behavior learned with RL will work well in the environment in which it’s been trained but will fail significantly in a similar but different environment. As a result, a simple tweak to the game’s dynamics requires that we discard the previous policy and train everything from scratch. During the summer of 2020, I developed a novel transfer learning algorithm tailored specifically to the incremental process of game development.

Problem: Game development is incremental; RL is not

Game development is incremental – a game usually starts from a simple prototype and gradually grows in complexity. However, RL is not incremental, and training a policy is time-consuming. Using ML-Agents in game development could become expensive, as a developer may need to wait several hours or even days to see how an

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