Title: Striving for Simplicity in Off-Policy Deep Reinforcement Learning. Abstract: This paper advocates the use of offline (batch) reinforcement learning (RL) to help (1) isolate the contributions of exploitation vs. exploration in off-policy deep RL, (2) improve reproducibility of deep RL research, and (3) facilitate the design of simpler deep RL algorithms. We propose an offline RL benchmark on Atari 2600 games comprising all of the replay data of a DQN agent. Using this benchmark, we demonstrate that recent off-policy deep RL algorithms, even when trained solely on logged DQN data, can outperform online DQN. We present Random Ensemble Mixture (REM), a simple Q-learning algorithm that enforces optimal Bellman consistency on random convex combinations of multiple Q-value estimates. The REM algorithm outperforms more complex RL agents such as C51 and QR-DQN on the offline Atari benchmark and performs comparably in the online setting.