At the heart of Voyage Deepdrive is a focus on end-to-end learning and deep reinforcement learning. At Voyage, we use simulation based development working with Applied Intuition, and that has driven our core production software forward. While this simulation use will continue to be our main development workflow, Deepdrive provides us a sandbox for research and exploring academic approaches. The reinforcement learning technique — which involves learning by trial and error — has shown tremendous promise in other complex tasks. Deepdrive’s first class support for reinforcement learning means that researchers can focus solely on the problem of driving rather than worrying about the stack required to run their agents on a physical car. We hope this will result in significant productivity gains for researchers wishing to iterate quickly on their ideas in this area.

By launching Voyage Deepdrive, we want to democratize self-driving car research by decoupling the algorithm development from the hardware. Our vision is to provide these tools to anyone who could benefit from them, while ensuring that their work can really benefit and progress the field.

A snapshot from Voyage Deepdrive

We do this by making sure that the simulator is guided by requirements from a real self-driving car company that faces these challenges everyday. We want to build worlds, scenarios, and events that have been historically hard to solve. We want to build an experimental sandbox for exploring deep reinforcement learning and other machine learning approaches that we hope can push the state-of-the-art in self-driving cars forward. And we want to reduce the barriers to entry so we can attract many more engineers to the field.

A snapshot from Voyage Deepdrive

As part of the release of Voyage Deepdrive, we are introducing some new and notable features. We are particularly excited about an ongoing leaderboard where teams and individuals can see how their agent stacks up against the rest. The simulator features a beautiful and accurate 3D world built by Parallel Domain, where a rich environment allows the testing of code to be more realistic. We have also introduced a feature where users can not only generate their own agents, but also generate new driving scenarios for agents to be tested against, which will help lead to more sophisticated algorithms.