  Offline reinforcement learning (offline RL), which aims to find an optimal
policy from a previously collected static dataset, bears algorithmic
difficulties due to function approximation errors from out-of-distribution
(OOD) data points. To this end, offline RL algorithms adopt either a constraint
or a penalty term that explicitly guides the policy to stay close to the given
dataset. However, prior methods typically require accurate estimation of the
behavior policy or sampling from OOD data points, which themselves can be a
non-trivial problem. Moreover, these methods under-utilize the generalization
ability of deep neural networks and often fall into suboptimal solutions too
close to the given dataset. In this work, we propose an uncertainty-based
offline RL method that takes into account the confidence of the Q-value
prediction and does not require any estimation or sampling of the data
distribution. We show that the clipped Q-learning, a technique widely used in
online RL, can be leveraged to successfully penalize OOD data points with high
prediction uncertainties. Surprisingly, we find that it is possible to
substantially outperform existing offline RL methods on various tasks by simply
increasing the number of Q-networks along with the clipped Q-learning. Based on
this observation, we propose an ensemble-diversified actor-critic algorithm
that reduces the number of required ensemble networks down to a tenth compared
to the naive ensemble while achieving state-of-the-art performance on most of
the D4RL benchmarks considered.
