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...
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...
在本文中侧重于 off-policy evaluation(OPE),主要研究的问题在于如何评估一个策略的优劣(or 输出的奖励值),可以看出在 online 或者 off line 中都存在这个问题,但是对于在线学习过程中,我们可以使用不同的策略来进行探索来收集无偏的数据;对于 off line 学习中,我们无法手机不同的策略,因此只能通过存在历史偏差的数据...
Off-policy evaluation (OPE) in reinforcement learning allows one to evaluate novel decision policies without needing to conduct exploration, which is often costly or otherwise infeasible. We consider for the first time the semiparametric efficiency limits of OPE in Markov decision processes (MDPs), ...
Off-policy evaluation (OPE) leverages data generated by other policies to evaluate a target policy. Previous OPE methods mainly focus on precisely estimating the true performance of a policy. We observe that in many applications, (1) the end goal of OPE is to compare two or multiple candidate...
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...
Unbiased recommender learning (URL) and off-policy evaluation/learning (OPE/L) techniques are effective in addressing the data bias caused by display position and logging policies, thereby consistently improving the performance of recommendations. However, when both bias exits in the logged data, these...
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 advantages of the proposed algorithms on the diagnostic ...
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...
policy import IPWLearner from obp.ope import ( OffPolicyEvaluation, RegressionModel, InverseProbabilityWeighting as IPW, DirectMethod as DM, DoublyRobust as DR, ) # (1) Generate Synthetic Bandit Data dataset = SyntheticBanditDataset(n_actions=10, reward_type="binary") bandit_feedback_train = ...