3 papers
cs.LG2024
Towards Provable Log Density Policy Gradient
Pulkit Katdare, Anant Joshi, Katherine Driggs-Campbell
Policy gradient methods are a vital ingredient behind the success of modern reinforcement learning. Modern policy gradient methods, although successful, introduce a residual error…
cs.LG2023
Marginalized Importance Sampling for Off-Environment Policy Evaluation
Pulkit Katdare, Nan Jiang, Katherine Driggs-Campbell
Reinforcement Learning (RL) methods are typically sample-inefficient, making it challenging to train and deploy RL-policies in real world robots. Even a robust policy trained in si…
cs.RO2021
Off Environment Evaluation Using Convex Risk Minimization
Pulkit Katdare, Shuijing Liu, Katherine Driggs-Campbell
Applying reinforcement learning (RL) methods on robots typically involves training a policy in simulation and deploying it on a robot in the real world. Because of the model mismat…