Reinforcement Learning for Adaptive Time-Stepping in the Chaotic Gravitational Three-Body Problem
We introduced the idea of using reinforcement learning algorithms to choose essential simulation parameters automatically. Here, we extend the idea to a more complex case, including multi-scale astrophysical systems. We create a method that balances accuracy and computation time while achieving better results than the current methods. Additionally, we ensure the robustness of the method for long simulations by creating a hybrid method that checks the quality of the reinforcement learning choices.
If you find it interesting, take a look at the Publication.
Also, the code and trained models are publicly available at Github link.
