Sparse Offline RL Robust to Data Corruption
Research Paper#Reinforcement Learning, Offline RL, Robustness, Sparsity🔬 Research|Analyzed: Jan 3, 2026 17:07•
Published: Dec 31, 2025 10:28
•1 min read
•ArXivAnalysis
This paper addresses the challenge of robust offline reinforcement learning in high-dimensional, sparse Markov Decision Processes (MDPs) where data is subject to corruption. It highlights the limitations of existing methods like LSVI when incorporating sparsity and proposes actor-critic methods with sparse robust estimators. The key contribution is providing the first non-vacuous guarantees in this challenging setting, demonstrating that learning near-optimal policies is still possible even with data corruption and specific coverage assumptions.
Key Takeaways
- •Addresses robust offline RL in high-dimensional, sparse MDPs.
- •Highlights limitations of LSVI when incorporating sparsity.
- •Proposes actor-critic methods with sparse robust estimators.
- •Provides the first non-vacuous guarantees under specific coverage and corruption assumptions.
- •Demonstrates the possibility of learning near-optimal policies even with data corruption.
Reference / Citation
View Original"The paper provides the first non-vacuous guarantees in high-dimensional sparse MDPs with single-policy concentrability coverage and corruption, showing that learning a near-optimal policy remains possible in regimes where traditional robust offline RL techniques may fail."