title: Learning Abstract Models for Long-Horizon Exploration
abstract: 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.
authors: Evan Zheran Liu Ramtin Keramati Sudarshan Seshadri Kelvin Guu Panupong Pasupat Emma Brunskill Percy Liang
hIndex: Percy Liang 60