Tag
This paper explores deep reinforcement learning for attitude control of spacecraft during hypersonic re-entry. It demonstrates that state-of-the-art RL and hybrid controllers can outperform traditional PID controllers with gain scheduling, especially when dynamics randomization is used to improve robustness and generalization.
Met-Shield is an open-source re-entry simulator that uses a Physics-Informed Neural Network (PINN) to predict thermal gradients on a spacecraft shield, integrated into a C++ WebAssembly engine for real-time browser performance. The project addresses robustness over traditional solvers but faces convergence issues during high heat flux phases.