Off-Policy Evaluation(OPE)是一种用于评估强化学习中的策略的方法,它利用行为策略采样的数据来评估目标策略的价值函数。OPE的目标是估计一个给定的目标策略的价值函数,从而了解该策略的性能。 OPE可以通过多种方法进行,其中包括Direct Method Estimator(DM)、Inverse Propensity Scoring(IPS)和Doubly Robust(DR)等。这些...
一、第一篇文章 首先看一下 off-policy value evaluation 研究的问题是什么。它希望通过 behavior policy 产生的轨迹,来估计另外一个策略的价值。 文章把 OPE 的算法分为以下三类。这里直接把文章的结论打印出来了,这里的 Table 2!对于只想看结论的同学,直接看这个表就好。 针对这三类,分别介绍以下各自最基本的版本。
DAI Bo老师提出的DICE(distribution correction estimation) family在OPE(Off-policy evaluation)问题中,在behavior-agnostic的数据上取得了SOTA的效果。本文将这些evaluation方法统一为同一线性规划的正则Lagrangian估计。这种统一将为改进DICE提供新帮助,将DICE扩展到一个更大的space,并实现更好的性能。更重要的是,通过数学...
This leads us to examine off-policy policy evaluation (OPE) in such settings. We focus on OPE for value-based methods, which are of particular interest in deep RL, with applications like robotics, where off-policy algorithms based on Q-function estimation can often attain better sample ...
We study distributional off-policy evaluation (OPE), of which the goal is to learn the distribution of the return for a target policy using offline data generated by a different policy. The theoretical foundation of many existing work relies on the supremum-extended statistical distances such as ...
of Illinois at Urbana-ChampaignUrbana, IL 61801nanjiang@illinois.eduJiawei HuangDepartment of Computer ScienceUniversity of Illinois at Urbana-ChampaignUrbana, IL 61801jiaweih@illinois.eduAbstractWe study minimax methods for off-policy evaluation (OPE) using value functionsand marginalized importance weights...
In this work, we consider the problem of estimating a behaviour policy for use in Off-Policy Policy Evaluation (OPE) when the true behaviour policy is unknown. Via a series of empirical studies, we demonstrate how accurate OPE is strongly dependent on the calibration of estimated behaviour polic...
[48].Recently, off-policy evaluation (OPE) methods have been proposed to tackle such challenges byestimating the performance of target (evaluation) RL policies with off l ine data, which only requiresthe trajectories collected over behavioral polices given a priori; similarly, off-policy selection ...
COBS is an Off-Policy Policy Evaluation (OPE) Benchmarking Suite. The goal is to provide fine experimental control to carefully tease out an OPE method's performance across many key conditions. We'd like to make this repo as useful as possible for the community. We commit to continual refac...
then apply the emphasis estimated by the proposed GEM(β) algorithm to the value estimation gradient and the policy gradient, respectively, yielding the corresponding ETD variant for off-policy evaluation (OPE) and actor-critic algorithm for off-policy control. Finally, we empirically demonstrate the...