47 citations · 72 across the 4 of their papers we have counts for
4 papers
Towards Painless Policy Optimization for Constrained MDPs
Arushi Jain, Sharan Vaswani, Reza Babanezhad +2
We study policy optimization in an infinite horizon, -discounted constrained Markov decision process (CMDP). Our objective is to return a policy that achieves large expected rew…
Digitize-PID: Automatic Digitization of Piping and Instrumentation Diagrams
Shubham Paliwal, Arushi Jain, Monika Sharma +1
Digitization of scanned Piping and Instrumentation diagrams(P&ID), widely used in manufacturing or mechanical industries such as oil and gas over several decades, has become a crit…
Variance Penalized On-Policy and Off-Policy Actor-Critic
Arushi Jain, Gandharv Patil, Ayush Jain +2
Reinforcement learning algorithms are typically geared towards optimizing the expected return of an agent. However, in many practical applications, low variance in the return is de…
Safe Option-Critic: Learning Safety in the Option-Critic Architecture
Arushi Jain, Khimya Khetarpal, Doina Precup
Designing hierarchical reinforcement learning algorithms that exhibit safe behaviour is not only vital for practical applications but also, facilitates a better understanding of an…