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...
解释一下One-step概念,这是针对要做 policy evaluation 价值评估的 RL-based 类 Offline RL 方法而言的。大多数这类方法都是基于 Bellman 等式做 TD-Learning 来评估价值的,整个过程服从广义策略迭代(GPI)框架,即迭代进行 policy evaluation 和 policy improvement 两步,其中 policy evaluation阶段:先用上一步迭代的...
Offline RL Without Off-Policy Evaluation, Brandfonbrener et al, 2021.NIPS.Algorithm: One-step algorithm. Offline Reinforcement Learning with Soft Behavior Regularization, Xu et al, 2021.arxiv.Algorithm: SBAC. Model-Based MOReL: Model-Based Offline Reinforcement Learning, Kidambi et al, 2020.TWIML....
Whitney, Rajesh Ranganath, Joan Bruna: “Offline RL Without Off-Policy Evaluation”, 2021; arXiv:2106.08909. 本论文由纽约大学(NYU)的David Brandfonbrener以第一作者提出,发表在NeurIPS 2021 顶会上【Accept (Spotlight)】,接收意见: While the method is very simple, the message is clear and the ...
Most prior approaches to offline reinforcement learning (RL) have taken an iterative actor-critic approach involving off-policy evaluation. In this paper we show that simply doing one step of constrained/regularized policy improvement using an on-policy Q estimate of the behavior policy performs surpr...
因为数据集与所学策略之间的 state-action 联合分布的偏移,会导致对 value 的高估,因此,标准的 off-policy RL 魔改成的 offline RL 可能会失败,尤其是在复杂和多模式数据分布上。 在本文中,我们提出了保守 Q-learning (CQL),旨在通过学习一个保守的 Q 函数来解决这些局限性,即,在该 Q 函数下,policy value ...
论文理解【OfflineRL】——【One-step】OfflineRLWithout Off-Policy Evaluation 标题:OfflineRLWithout Off-Policy Evaluation;发表:NI Offline RL 离线强化学习 one-step 迭代 lua 原创 云端FFF 2023-03-24 14:30:52 438阅读 论文理解【OfflineRL】—— A dataset perspective onofflinereinforcement learning ...
1.3 Offline RL方法 方法1:基于重要采样的离线RL与离线策略评估 重要采样法进行离线策略估计(Off-Policy Evaluation via Importance Sampling): (1)利用importance sampling,和训练得到的proposal distribution求真实π无偏估计。 (2)缺点:variance太高了。 (3)改进:doubly robust estimator ...
SCOPE-RL: A python library for offline reinforcement learning, off-policy evaluation, and selection researchreinforcement-learningrisk-assessmentoff-policy-evaluationoffline-rl UpdatedMar 18, 2024 Python denisyarats/exorl Star100 Code Issues Pull requests ...
Offline RL: Theory/Methods Offline RL: Benchmarks/Experiments Offline RL: Applications Off-Policy Evaluation and Learning: Theory/Methods Off-Policy Evaluation: Contextual Bandits Off-Policy Evaluation: Reinforcement Learning Off-Policy Learning Off-Policy Evaluation and Learning: Benchmarks/Experiments ...