36 citations · 237 across the 33 of their papers we have counts for
4 papers · 1 filter
Action Noise in Off-Policy Deep Reinforcement Learning: Impact on Exploration and Performance
Jakob Hollenstein, Sayantan Auddy, Matteo Saveriano +2
Many Deep Reinforcement Learning (D-RL) algorithms rely on simple forms of exploration such as the additive action noise often used in continuous control domains. Typically, the sc…
Using Bayesian Deep Learning to infer Planet Mass from Gaps in Protoplanetary Disks
Sayantan Auddy, Ramit Dey, Min-Kai Lin +2
Planet induced sub-structures, like annular gaps, observed in dust emission from protoplanetary disks provide a unique probe to characterize unseen young planets. While deep learni…
Continual Learning from Demonstration of Robotics Skills
Sayantan Auddy, Jakob Hollenstein, Matteo Saveriano +2
Methods for teaching motion skills to robots focus on training for a single skill at a time. Robots capable of learning from demonstration can considerably benefit from the added a…
The Magnetic Field versus Density relation in Star-Forming Molecular Clouds
Sayantan Auddy, Shantanu Basu, Takahiro Kudoh
We study the magnetic field to density () relation in turbulent molecular clouds with dynamically important magnetic fields using nonideal three-dimensional magnetohydrodynami…