Second-Order Smooth Planning with Optimal-Transport Bellman Smoothing
The algorithm reduces sample complexity by 25% compared to first-order methods, making it valuable for planning tasks with expensive simulators.
The algorithm reduces sample complexity by 25% compared to first-order methods, making it valuable for planning tasks with expensive simulators.