  Offline Reinforcement Learning (RL) faces distributional shift and unreliable
value estimation, especially for out-of-distribution (OOD) actions. To address
this, existing uncertainty-based methods penalize the value function with
uncertainty quantification and demand numerous ensemble networks, posing
computational challenges and suboptimal outcomes. In this paper, we introduce a
novel strategy employing diverse randomized value functions to estimate the
posterior distribution of $Q$-values. It provides robust uncertainty
quantification and estimates lower confidence bounds (LCB) of $Q$-values. By
applying moderate value penalties for OOD actions, our method fosters a
provably pessimistic approach. We also emphasize on diversity within randomized
value functions and enhance efficiency by introducing a diversity
regularization method, reducing the requisite number of networks. These modules
lead to reliable value estimation and efficient policy learning from offline
data. Theoretical analysis shows that our method recovers the provably
efficient LCB-penalty under linear MDP assumptions. Extensive empirical results
also demonstrate that our proposed method significantly outperforms baseline
methods in terms of performance and parametric efficiency.
