Bonsai was built on the core belief that the best way for machines to learn is to teach them.
Just like we teach humans to solve problems, so can we teach machines using the expertise we already have. We can break down complex problems into simpler concepts, and let them practice solving those concepts one-by-one until a larger problem is solved.
But there’s one more essential element: simulations.
Simulations are key to building applied deep reinforcement learning models. These digital replicas of real-world environments allow enterprises to test and validate processes in simulation before trying them out on real, and expensive, systems.
There are many simulators on the market today, being used across a range of industries to assist subject matter experts in modeling the best processes and systems for their business problems.One simulator in particular, Matlab/Simulink, is extremely well-suited for modeling industrial control systems, and machine tuning processes, that can be very well optimized with deep reinforcement learning. But until now, applying cutting-edge AI to existing Simulink models has not been possible for the subject matter experts most familiar with the simulations themselves.
Today we’re excited to announce a new Simulink connector for the Bonsai Platform and the launch of the Bonsai Simulink Beta program. These tools apply deep reinforcement learning to existing Simulink models and enhance the performance of your real-world systems.
Anyone enrolled in the Bonsai Simulink Beta program can leverage the Bonsai Platform to build deep reinforcement learning directly into their existing Simulink models. This program offers participants:
Apply now to the Bonsai Simulink Beta program, and start optimizing your Simulink model with deep reinforcement learning: