Tradeoff in offline RL: do Q-learning’s compounding errors outweigh the benefits of learning behavioral values?

Ascertain whether, in offline reinforcement learning, the compounding errors inherent to temporal-difference Q-learning outweigh the benefits of learning the behavioral value function Q^β with 1-step (Monte Carlo) methods rather than the optimal value function Q^*.

Background

The offline RL literature often contrasts 1-step (Monte Carlo) methods, which mitigate bootstrapping errors, with multi-step (Q-learning) methods, which aim to learn optimal values but can accumulate errors. Despite theoretical preferences for optimality, empirical results show strong performance for 1-step methods, prompting a fundamental question about whether the error dynamics in Q-learning negate its advantages.

This open question frames a central uncertainty in choosing value estimation strategies in offline RL and motivates approaches that attempt to combine stability from Monte Carlo learning with mechanisms to recover optimal policies.

References

However, their strong performance over the years continues to suggest that it remains an open question whether the compounding errors of Q-learning outweigh the benefits from learning the behavioral value function, rather than the value function of the optimal policy.

— Offline Goal-conditioned Reinforcement Learning with Quasimetric Representations  (2509.20478 - Myers et al., 24 Sep 2025) in Section 2.2 (Offline Reinforcement Learning)

A method that keeps the actor near the data, such as TD3+BC , is the proper test, which we leave open.

— Coupled State-Space Modelling, Control, and Policy Distillation for Hybrid Rigid-Pneumatic Manipulators  (2609.29424 - Samuel et al., 24 Sep 2026) in Section 6, subsection “Placed labels close half the gap”