3 citations · 3 across the 7 of their papers we have counts for
7 papers
Surface-mediated frequency aging beyond quality-factor saturation in an AlScN-on-silicon resonator
S M Enamul Hoque Yousuf, Mahmudul Hasan, Shruti Mishra +3
Vacuum package integrity in micro- and nanoelectromechanical resonators is commonly assessed through the quality factor Q, although Q probes residual-gas damping rather than the su…
Coherent phononic frequency combs in ferroelectric CMOS oxides
Jinghan Gao, Shruti Mishra, Yilin Kou +3
Modern electronic systems require tens of clock and carrier frequencies, each synthesized by a dedicated phase-locked loop from a shared reference, imposing routing, power and sync…
Coachable agents for interactive gameplay
Roberto Capobianco, Harm van Seijen, Nolan D. Bard +38
Reinforcement learning has proven to be a valuable tool in the creation of advanced AI and robotic systems, contributing to everything from game playing to robotics to foundation m…
A perspective on fluid mechanical environments for challenges in reinforcement learning
Shruti Mishra, Michael Chang, Vamsi Spandan +1
We consider the challenge of developing agents that efficiently interact with high-dimensional, evolving environments, towards a view of practical reinforcement learning (RL) agent…
Physical Reinforcement Learning
Sam Dillavou, Shruti Mishra
Digital computers are power-hungry and largely intolerant of damaged components, making them potentially difficult tools for energy-limited autonomous agents in uncertain environme…
Policy composition in reinforcement learning via multi-objective policy optimization
Shruti Mishra, Ankit Anand, Jordan Hoffmann +4
We enable reinforcement learning agents to learn successful behavior policies by utilizing relevant pre-existing teacher policies. The teacher policies are introduced as objectives…
On Multi-objective Policy Optimization as a Tool for Reinforcement Learning: Case Studies in Offline RL and Finetuning
Abbas Abdolmaleki, Sandy H. Huang, Giulia Vezzani +11
Many advances that have improved the robustness and efficiency of deep reinforcement learning (RL) algorithms can, in one way or another, be understood as introducing additional ob…