arXiv:2507. 15356v2 Announce Type: replace Abstract: Offline reinforcement learning (RL) learns policies from fixed datasets, thereby avoiding costly or unsafe environment interactions.
Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.
arXiv:2502. 19544v3 Announce Type: replace Abstract: Leveraging offline data is a promising way to improve the sample efficiency of online reinforcement learning (RL).
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...
arXiv:2606. 09115v1 Announce Type: new Abstract: Offline reinforcement learning (RL) offers a path to policy improvement from logged data alone, using historical returns or other measurable outcomes as world feedback.
arXiv:2608. 11363v1 Announce Type: cross Abstract: A central goal in robot learning is to move beyond task-specific human data collection toward robots that improve through autonomous interaction.
arXiv:2605.13054v2 Announce Type: replace-cross Abstract: Cross-domain offline reinforcement learning learns a target policy from pre-collected source and target datasets with different dynamics. Whe...
The paper introduces BADA, a Boundary-Aware Data Augmentation technique for offline reinforcement learning. By interpolating neighboring states to create synthetic data that respects the original distribution, BADA improves in-distribution generalization and robustness. Experiments on limited offline datasets show that BADA achieves state-of-the-art performance across diverse benchmarks.