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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…