| Paper | Notes | Author | Summary |
|---|---|---|---|
| Convexity and Convergence in Gradient Descent | HackMD | Sharath | In this, we talk about strong and smooth convex functions and their convergence rates in case of gradient descent |
| Notes on Stability of Dynamical Systems | HackMD | Sharath | In this notes, we discuss discrete and continuous dynamical systems and their stability properties |
| Policy Gradient Theorem | HackMD | Raj | Derivation and explanation for the policy gradient theorem in Reinforcement Learning |
| Reproducing Kernel Hilbert Spaces | HackMD | Sharath | These notes review some of the fundamental concepts of linear algebra like vector spaces, inner product spaces, etc., and then introduces the basic concepts of RKHS. |
| Guaranteed computation of robot trajectories | HackMD | Uddesh | A contractor-based approach is proposed for guaranteed integration of state equations. The framework is based on the use of tubes as envelopes of feasible trajectories. |