1)off-policy评估概述 2)Direct Method Estimator(DM) 3) Inverse Propensity Scoring(IPS) 4) Doubly Robust(DR) 1. 概述 1.1 off-policy定义 强化学习中的off-policy方法采用两个策略,一个用来学习并最终成为最优策略,另一个具有试探性,用来产生智能体的行为样本。用来学习的策略被称为目标策略,用于生成样本的...
Off-policy Evaluation in Infinite-Horizon Reinforcement Learning with Latent ConfoundersAndrew BennettNathan KallusLihong LiAli MousaviPMLRInternational Conference on Artificial Intelligence and Statistics
off-policy evaluation 评估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...
原论文链接:Offline RL Without Off-Policy Evaluation one-step and multi-step Gulcehre et al.[1]中展示的这张图片可以很清楚的表现 one-step(这篇文章称为 behavior value estimation) 和 multi-step 之间的差别。offline setting 下,之前的方法(例如 BCQ,CQL)都是策略评估和策略改进之间交替进行的,而 one...
off-policy评估是强化学习领域中的一种重要技术，它允许我们在不需要使用目标策略直接采样数据的情况下，对策略进行评估。这一方法通常涉及到两个策略：目标策略和行为策略。目标策略用于评估，而行为策略则用于产生数据。在off-policy评估过程中，我们面临的主要挑战包括部分可观测性问题和反事实推理。部分可...
Monte Carlo(MC) Off Policy Evaluation 目标:在给定由行为策略π 2 \pi_2π2产生的轮次(episodes)下,评估策略π 1 \pi_1π1的价值V π ( s ) V^\pi(s)Vπ(s) s 1 , a 1 , r 1 , s 2 , a 2 , r 2 , . . . . s_1,a_1,r_1,s_2,a_2,r_2,...s1,a1...
摘要:先前的大多数 Offline-RL 方法都采用了涉及 Off-policy evaluation 的迭代 Actor-Critic (AC) 方法。本文中我们证明了只需简单地基于 behavior policy 的 on-policy 。这种 One-step 算法在大部分 D4RL benchmark 上击败了之前的迭代算法。这种 One-step baseline 在实现强大性能的同时,比以前提出的迭代算...
Off-Policy Evaluation For Slate Recommendation Adith Swaminathan, Akshay Krishnamurthy, Alekh Agarwal, Miro Dudík, John Langford, Damien Jose, Imed Zitouni 2017 Neural Information Processing Systems|December 2017 下载BibTex This paper studies the evaluation of policies that recommend an ordered set of...
When learning from a batch of logged bandit feedback, the discrepancy between the policy to be learned and the off-policy training data imposes statistical and computational challenges. Unlike classical supervised learning and online learning settings, in batch contextual bandit learning, one only has...
We study the problem of off-policy policy evaluation (OPPE) in RL. In contrast to prior work, we consider how to estimate both the individual policy value and average policy value accurately. We draw inspiration from recent work in causal reasoning, and propose a new finite sample generalizatio...