  Off-policy reinforcement learning (RL) using a fixed offline dataset of
logged interactions is an important consideration in real world applications.
This paper studies offline RL using the DQN replay dataset comprising the
entire replay experience of a DQN agent on 60 Atari 2600 games. We demonstrate
that recent off-policy deep RL algorithms, even when trained solely on this
fixed dataset, outperform the fully trained DQN agent. To enhance
generalization in the offline setting, we present Random Ensemble Mixture
(REM), a robust Q-learning algorithm that enforces optimal Bellman consistency
on random convex combinations of multiple Q-value estimates. Offline REM
trained on the DQN replay dataset surpasses strong RL baselines. Ablation
studies highlight the role of offline dataset size and diversity as well as the
algorithm choice in our positive results. Overall, the results here present an
optimistic view that robust RL algorithms trained on sufficiently large and
diverse offline datasets can lead to high quality policies. The DQN replay
dataset can serve as an offline RL benchmark and is open-sourced.
