  To obtain higher sample efficiency and superior final performance
simultaneously has been one of the major challenges for deep reinforcement
learning (DRL). Previous work could handle one of these challenges but
typically failed to address them concurrently. In this paper, we try to tackle
these two challenges simultaneously. To achieve this, we firstly decouple these
challenges into two classic RL problems: data richness and
exploration-exploitation trade-off. Then, we cast these two problems into the
training data distribution optimization problem, namely to obtain desired
training data within limited interactions, and address them concurrently via i)
explicit modeling and control of the capacity and diversity of behavior policy
and ii) more fine-grained and adaptive control of selective/sampling
distribution of the behavior policy using a monotonic data distribution
optimization. Finally, we integrate this process into Generalized Policy
Iteration (GPI) and obtain a more general framework called Generalized Data
Distribution Iteration (GDI). We use the GDI framework to introduce
operator-based versions of well-known RL methods from DQN to Agent57.
Theoretical guarantee of the superiority of GDI compared with GPI is concluded.
We also demonstrate our state-of-the-art (SOTA) performance on Arcade Learning
Environment (ALE), wherein our algorithm has achieved 9620.33% mean human
normalized score (HNS), 1146.39% median HNS and surpassed 22 human world
records using only 200M training frames. Our performance is comparable to
Agent57's while we consume 500 times less data. We argue that there is still a
long way to go before obtaining real superhuman agents in ALE.
