  A grand challenge in reinforcement learning is intelligent exploration,
especially when rewards are sparse or deceptive. Two Atari games serve as
benchmarks for such hard-exploration domains: Montezuma's Revenge and Pitfall.
On both games, current RL algorithms perform poorly, even those with intrinsic
motivation, which is the dominant method to improve performance on
hard-exploration domains. To address this shortfall, we introduce a new
algorithm called Go-Explore. It exploits the following principles: (1) remember
previously visited states, (2) first return to a promising state (without
exploration), then explore from it, and (3) solve simulated environments
through any available means (including by introducing determinism), then
robustify via imitation learning. The combined effect of these principles is a
dramatic performance improvement on hard-exploration problems. On Montezuma's
Revenge, Go-Explore scores a mean of over 43k points, almost 4 times the
previous state of the art. Go-Explore can also harness human-provided domain
knowledge and, when augmented with it, scores a mean of over 650k points on
Montezuma's Revenge. Its max performance of nearly 18 million surpasses the
human world record, meeting even the strictest definition of "superhuman"
performance. On Pitfall, Go-Explore with domain knowledge is the first
algorithm to score above zero. Its mean score of almost 60k points exceeds
expert human performance. Because Go-Explore produces high-performing
demonstrations automatically and cheaply, it also outperforms imitation
learning work where humans provide solution demonstrations. Go-Explore opens up
many new research directions into improving it and weaving its insights into
current RL algorithms. It may also enable progress on previously unsolvable
hard-exploration problems in many domains, especially those that harness a
simulator during training (e.g. robotics).
