arXiv (All)
open access
UBA-ORL: Unlearning-Activated Backdoor Attacks on Offline Reinforcement Learning
Offline reinforcement learning (offline RL) enables policy learning from pre-collected static datasets without online exploration, and is increasingly deployed not only in safety-critical domains such as autonomous driving and robotic control but also in data-mining applications such as recommendati
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Robust Adversarial Reinforcement Learning with Risk Sensitivity and Critic Consistency Regularization#1064211
- Official Review · ICLR 2025#787007
- Certifying Lower Bounds for Risk-Sensitive Reinforcement Learning under Adversarial State Perturbations#997841
- Rethinking Learnability in Offline Data-driven Optimization#822048
- Trust the Batch, On- or Off-Policy: Adaptive Policy Optimization for RL Post-Training#178888
Record · ID 1037394
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive