Leveraging Topological Maps in Deep Reinforcement Learning for
  Multi-Object Navigation
Simon Hakenes Tobias Glasmachers
This work addresses the challenge of navigating expansive spaces with sparse
rewards through Reinforcement Learning (RL). Using topological maps, we elevate
elementary actions to object-oriented macro actions, enabling a simple Deep
Q-Network (DQN) agent to solve otherwise practically impossible environments.