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Found 3 papers, 1 papers with code
Efficiently Escaping Saddle Points for Non-Convex Policy Optimization
Policy gradient (PG) is widely used in reinforcement learning due to its scalability and good performance.
Momentum-Based Policy Gradient with Second-Order Information
SHARP algorithm is parameter-free, achieving $\epsilon$-approximate first-order stationary point with $O(\epsilon^{-3})$ number of trajectories, while using a batch size of $O(1)$ at each iteration.
SVG-Net: An SVG-based Trajectory Prediction Model
Finally, we illustrate how, by using SVG, one can benefit from datasets and advancements in other research fronts that also utilize the same input format.
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