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Progress in deep reinforcement learning (RL) research is largely enabled by benchmark task environments. However, analyzing the nature of those environments is often overlooked. In particular, we still do not have agreeable ways to measure the …

We propose a novel method that achieves both high sample-efficiency in offline RL and "deployment-efficiency" in online RL.