arXiv Machine Learning

CoDrift: Compositional Drifting for Offline Reinforcement Learning

arXiv Machine Learning

Aug 24

Decoupling Policy Extraction for Offline Reinforcement Learning

arXiv:2608.20909v1 Announce Type: new Abstract: Offline RL methods commonly jointly train the actor and critic, where the critic is used to guide the actor toward higher-value actions. This coupled l...

By Xuyao Lin, Yixiang Shan, Jinru Duan, Tao Yang, Xinyu Zhao, Runyu Lei, Yiming Zhao, Jiaxin Fan, Zongbao Feng, Peng Jia

rag reinforcement-learning

arXiv Machine Learning

3d ago

PathBridger: Subgoal Bridges for Offline Goal-Conditioned Reinforcement Learning

arXiv:2608.29061v1 Announce Type: new Abstract: Offline goal-conditioned reinforcement learning (GCRL) aims to learn policies for reaching diverse goals entirely from fixed trajectory data. Long-hori...

By Soohyun Choi, Seonvin Cho, Songnam Hong

reinforcement-learning robotics

arXiv Machine Learning

Jul 28

Hierarchical Reinforcement Learning with Optimal Level Synchronization Based on Flow-Based Deep Generative Model

arXiv:2107. 08183v2 Announce Type: replace Abstract: High-dimensional state and action spaces combined with sparse reward structures in reinforcement learning (RL) environments typically require advanced control architectures.

By JaeYoon Kim, Junyu Xuan, Christy Liang, Farookh Hussain

reinforcement-learning benchmarks

arXiv Machine Learning

Jun 16

Efficient Reinforcement Learning by Guiding World Models with Non-Curated Data

arXiv:2502. 19544v3 Announce Type: replace Abstract: Leveraging offline data is a promising way to improve the sample efficiency of online reinforcement learning (RL).

By Yi Zhao, Aidan Scannell, Wenshuai Zhao, Yuxin Hou, Tianyu Cui, Le Chen, Dieter B\"uchler, Arno Solin, Juho Kannala, Joni Pajarinen

reinforcement-learning robotics fine-tuning

arXiv AI

Jul 17

RAD: Retrieval High-quality Demonstrations to Enhance Decision-making

arXiv:2507. 15356v2 Announce Type: replace Abstract: Offline reinforcement learning (RL) learns policies from fixed datasets, thereby avoiding costly or unsafe environment interactions.

By Lu Guo, Yixiang Shan, Zhengbang Zhu, Qifan Liang, Lichang Song, Ting Long, Weinan Zhang, Yi Chang

agents reinforcement-learning benchmarks

arXiv Machine Learning

1d ago

Recursive Value Learning for Long-Horizon Offline Goal-Conditioned RL

arXiv:2609.02237v1 Announce Type: new Abstract: Scaling offline goal-conditioned reinforcement learning (GCRL) to long-horizon tasks is difficult because (1) long-range value learning depends on shor...

By Hyeonseong Jeon, Youngwoon Lee

reinforcement-learning

arXiv AI

Jul 24

Expert Behavior Prior Reinforcement Learning

arXiv:2607. 21302v1 Announce Type: new Abstract: Behavior prior reinforcement learning (BPRL) has emerged as a promising paradigm to improve sample efficiency in online reinforcement learning (RL) by leveraging policy priors derived from offline demonstrations.

By Gong Gao, Weidong Zhao, Xianhui Liu, Ning Jia

agents reinforcement-learning robotics benchmarks

arXiv AI

Aug 21

Learning Hierarchical Skill Policies with Offline Quality-Diversity Reinforcement Learning

arXiv:2608. 19684v1 Announce Type: new Abstract: Recent studies investigate how to leverage pre-collected datasets to improve the policy performance and sample efficiency of RL.

By Tanachai Anakewat, Takayuki Osa, Tatsuya Harada

reinforcement-learning robotics

arXiv Machine Learning

Jul 9

Latent Policy Steering through One-Step Flow Policies

arXiv:2603. 05296v2 Announce Type: replace-cross Abstract: Offline reinforcement learning (RL) allows robots to learn from offline datasets without risky exploration.

By Hokyun Im, Andrey Kolobov, Jianlong Fu, Youngwoon Lee

reinforcement-learning robotics efficiency benchmarks

arXiv AI

Jul 23

Leveraging Offline Supervision for Efficient and Generalizable Reinforcement Learning in Large-Scale Vision-Language-Action Models

arXiv:2607. 19399v1 Announce Type: cross Abstract: It is commonly observed that online reinforcement learning (RL) produces better-performing strategies than offline methods across a broad range of performance measures.

By Dmitriy Poyarkov, Aleksei Staroverov, Aleksandr I. Panov

reinforcement-learning fine-tuning multimodal benchmarks

arXiv Machine Learning

Aug 4

ReBRAC-v2: The Return of the King

arXiv:2608. 01205v1 Announce Type: new Abstract: Recent offline reinforcement learning methods increasingly rely on expressive generative policies and specialized value-guidance mechanisms.

By Denis Tarasov, Robert K. Katzschmann

reinforcement-learning benchmarks

arXiv Machine Learning

Jul 24

Offline RL with Hierarchical Action Chunking

arXiv:2607. 20834v1 Announce Type: new Abstract: Offline goal-conditioned reinforcement learning (RL) holds the promise of learning general-purpose policies from static datasets.

By Ahad Jawaid

rag reinforcement-learning

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