On the Complexity of Offline Reinforcement Learning with $Q^\star$-Approximation and Partial Coverage
arXiv:2602.12107v2 Announce Type: replace Abstract: We study offline reinforcement learning under $Q^\star$-approximation and partial coverage, a setting that motivates practical algorithms such as Conservative $Q$-Learning (CQL; Kumar et al., 2020) but has received limited theoretical attention. Our work is inspired by the following open question: "Are $Q^\star$-realizability and Bellman completeness sufficient for sample-efficient offline RL under partial coverage?" We answer in the negative via an information-theoretic lower bound. To identify additional structure that enables sample-effici
This paper addresses fundamental theoretical limitations in offline reinforcement learning, a critical area for developing robust AI agents without extensive real-world interaction.
Understanding the theoretical boundaries of offline RL directly impacts the design and application of RL algorithms, particularly in fields where data collection is expensive or risky.
This research suggests that current assumptions about Q*-realizability and Bellman completeness are insufficient for sample-efficient offline RL under partial coverage, prompting the need for new theoretical frameworks and algorithmic approaches.
- · AI researchers focusing on theoretical foundations
- · Developers of robust offline RL algorithms
- · Industries with high-cost data collection
- · Practitioners relying solely on current offline RL assumptions without deeper th
This research provides a negative answer regarding the sufficiency of certain conditions for sample-efficient offline RL, highlighting existing gaps.
It will likely trigger a new wave of research into alternative structures and conditions required for robust offline reinforcement learning.
Improved theoretical understanding could lead to more reliable and deployable AI agents in complex, safety-critical environments.
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