AlgaeDICE: Policy Gradient from Arbitrary Experience
Abstract
In many real-world applications of reinforcement learning (RL), interactions with the environment are limited due to cost or feasibility. This presents a challenge to traditional RL algorithms since the max-return objective involves an expectation over on-policy samples. We introduce a new formulation of max-return optimization that allows the problem to be re-expressed by an expectation over an arbitrary behavior-agnostic and off-policy data distribution. We first derive this result by considering a regularized version of the dual max-return objective before extending our findings to unregularized objectives through the use of a Lagrangian formulation of the linear programming characterization of Q-values. We show that, if auxiliary dual variables of the objective are optimized, then the gradient of the off-policy objective is exactly the on-policy policy gradient, without any use of importance weighting. In addition to revealing the appealing theoretical properties of this approach, we also show that it delivers good practical performance.
Cite
@article{arxiv.1912.02074,
title = {AlgaeDICE: Policy Gradient from Arbitrary Experience},
author = {Ofir Nachum and Bo Dai and Ilya Kostrikov and Yinlam Chow and Lihong Li and Dale Schuurmans},
journal= {arXiv preprint arXiv:1912.02074},
year = {2019}
}