Sparse Graphical Memory for Robust Planning
This addresses the challenge of robust planning for agents in real-world environments, combining deep learning with classical planning to improve scalability and performance in long-horizon, sparse-reward scenarios.
The paper tackles the problem of scaling deep reinforcement learning to long-horizon tasks from high-dimensional sensory input by introducing Sparse Graphical Memory (SGM), a data structure that stores states and transitions sparsely using a two-way consistency objective, and shows it significantly outperforms state-of-the-art methods on visual navigation tasks.
To operate effectively in the real world, agents should be able to act from high-dimensional raw sensory input such as images and achieve diverse goals across long time-horizons. Current deep reinforcement and imitation learning methods can learn directly from high-dimensional inputs but do not scale well to long-horizon tasks. In contrast, classical graphical methods like A* search are able to solve long-horizon tasks, but assume that the state space is abstracted away from raw sensory input. Recent works have attempted to combine the strengths of deep learning and classical planning; however, dominant methods in this domain are still quite brittle and scale poorly with the size of the environment. We introduce Sparse Graphical Memory (SGM), a new data structure that stores states and feasible transitions in a sparse memory. SGM aggregates states according to a novel two-way consistency objective, adapting classic state aggregation criteria to goal-conditioned RL: two states are redundant when they are interchangeable both as goals and as starting states. Theoretically, we prove that merging nodes according to two-way consistency leads to an increase in shortest path lengths that scales only linearly with the merging threshold. Experimentally, we show that SGM significantly outperforms current state of the art methods on long horizon, sparse-reward visual navigation tasks. Project video and code are available at https://mishalaskin.github.io/sgm/