20 citations · 150 across the 43 of their papers we have counts for
5 papers · 1 filter
Quasi-Newton Trust Region Policy Optimization
Devesh Jha, Arvind Raghunathan, Diego Romeres
We propose a trust region method for policy optimization that employs Quasi-Newton approximation for the Hessian, called Quasi-Newton Trust Region Policy Optimization QNTRPO. Gradi…
Learning Deep Parameterized Skills from Demonstration for Re-targetable Visuomotor Control
Jonathan Chang, Nishanth Kumar, Sean Hastings +6
Robots need to learn skills that can not only generalize across similar problems but also be directed to a specific goal. Previous methods either train a new skill for every differ…
Safe Approximate Dynamic Programming Via Kernelized Lipschitz Estimation
Ankush Chakrabarty, Devesh K. Jha, Gregery T. Buzzard +2
We develop a method for obtaining safe initial policies for reinforcement learning via approximate dynamic programming (ADP) techniques for uncertain systems evolving with discrete…
Game Theoretic Optimization via Gradient-based Nikaido-Isoda Function
Arvind U. Raghunathan, Anoop Cherian, Devesh K. Jha
Computing Nash equilibrium (NE) of multi-player games has witnessed renewed interest due to recent advances in generative adversarial networks. However, computing equilibrium effic…
Trajectory Optimization for Unknown Constrained Systems using Reinforcement Learning
Kei Ota, Devesh K. Jha, Tomoaki Oiki +4
In this paper, we propose a reinforcement learning-based algorithm for trajectory optimization for constrained dynamical systems. This problem is motivated by the fact that for mos…