Leveraging Topological Maps in Deep Reinforcement Learning for Multi-Object Navigation
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Leveraging Topological Maps in Deep Reinforcement Learning for Multi-Object Navigation
Simon Hakenes, Tobias Glasmachers
AbstractThis 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.