Learning Abstract Models for Long-Horizon Exploration

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.

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