Distributionally Robust Reinforcement Learning with Interactive Data Collection: Fundamental Hardness and Near-Optimal Algorithms

2026-07-14 · Source: stat.ML updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems · Depth: Expert, extended

Summary

The paper "Distributionally Robust Reinforcement Learning with Interactive Data Collection: Fundamental Hardness and Near-Optimal Algorithms" addresses the sim-to-real gap in reinforcement learning (RL) using distributionally robust RL (RMDPs). It first establishes a fundamental hardness result, demonstrating that sample-efficient learning in robust RL with interactive data collection is generally unattainable due to the "curse of support shift," leading to an Ω(ρ⋅H⋅K)-online regret lower bound. To overcome this, the authors introduce the "vanishing minimal value assumption" for RMDPs with total-variation (TV) distance robust sets, which effectively mitigates the support shift issue. Under this assumption, they propose OPROVI-TV, the first provably sample-efficient algorithm for this paradigm. OPROVI-TV achieves a sample complexity of O~(min{H,ρ⁻¹}⋅H²SA/ε²), matching minimax-optimal bounds for standard MDPs when ρ=0 and showing improved efficiency as ρ approaches 1.

Key takeaway

For AI Scientists and students developing robust reinforcement learning agents, recognize that interactive data collection in RMDPs faces a "curse of support shift" making sample-efficient learning generally intractable. If your problem context allows for a "vanishing minimal value assumption" or has fail-states, consider implementing the OPROVI-TV algorithm. This approach offers provable sample-efficiency, matching or exceeding prior generative model methods, and is particularly beneficial when the robust set radius (ρ) is large.

Key insights

Interactive distributionally robust RL is fundamentally hard without assumptions, but solvable with a vanishing minimal value.

Principles

• Support shift between training and testing environments causes fundamental hardness in interactive robust RL.
• The "vanishing minimal value assumption" can mitigate support shift issues in TV-distance RMDPs.
• Increased robust set radius (ρ) can decrease sample complexity in robust RL.

Method

OPROVI-TV uses empirical transition estimation, optimistic robust planning with upper/lower value bounds, and a variance-aware bonus function to balance exploration, exploitation, and distributional robustness.

In practice

• Consider the "vanishing minimal value assumption" when designing interactive robust RL systems.
• Implement OPROVI-TV for sample-efficient robust policy learning in TV-distance RMDPs.

Topics

• Distributionally Robust Reinforcement Learning
• Robust Markov Decision Processes
• Interactive Data Collection
• Sample Complexity
• Curse of Support Shift
• OPROVI-TV Algorithm
• Total-Variation Distance

Best for: Research Scientist, AI Scientist, AI Student

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Editorial summary, takeaway, and curation by AIssential. Original article published by stat.ML updates on arXiv.org.