In high-dimensional reinforcement learning settings with sparse rewards, performing
 effective exploration to even obtain any reward signal is an open challenge.
 While model-based approaches hold promise of better exploration via planning, it
 is extremely difficult to learn a reliable enough Markov Decision Process (MDP)
 in high dimensions (e.g., over 10^100 states). In this paper, we propose learning
 an abstract MDP over a much smaller number of states (e.g., 10^5), which we can
 plan over for effective exploration. We assume we have an abstraction function
 that maps concrete states (e.g., raw pixels) to abstract states (e.g., agent position,
 ignoring other objects). In our approach, a manager maintains an abstract
 MDP over a subset of the abstract states, which grows monotonically through targeted
 exploration (possible due to the abstract MDP). Concurrently, we learn a
 worker policy to travel between abstract states; the worker deals with the messiness
 of concrete states and presents a clean abstraction to the manager. On three of
 the hardest games from the Arcade Learning Environment (Montezuma's,
 Pitfall!, and Private Eye), our approach outperforms the previous
 state-of-the-art by over a factor of 2 in each game. In Pitfall!, our approach is
 the first to achieve superhuman performance without demonstrations.
We automatically construct and explore a small abstract Markov Decision Process, enabling us to achieve state-of-the-art results on Montezuma's Revenge, Pitfall!, and Private Eye by a significant margin.