  Across the Arcade Learning Environment, Rainbow achieves a level of
performance competitive with humans and modern RL algorithms. However,
attaining this level of performance requires large amounts of data and hardware
resources, making research in this area computationally expensive and use in
practical applications often infeasible. This paper's contribution is
threefold: We (1) propose an improved version of Rainbow, seeking to
drastically reduce Rainbow's data, training time, and compute requirements
while maintaining its competitive performance; (2) we empirically demonstrate
the effectiveness of our approach through experiments on the Arcade Learning
Environment, and (3) we conduct a number of ablation studies to investigate the
effect of the individual proposed modifications. Our improved version of
Rainbow reaches a median human normalized score close to classic Rainbow's,
while using 20 times less data and requiring only 7.5 hours of training time on
a single GPU. We also provide our full implementation including pre-trained
models.
