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Learning Exploration Policies for Navigation
[article]
2019
arXiv
pre-print
Numerous past works have tackled the problem of task-driven navigation. But, how to effectively explore a new environment to enable a variety of down-stream tasks has received much less attention. In this work, we study how agents can autonomously explore realistic and complex 3D environments without the context of task-rewards. We propose a learning-based approach and investigate different policy architectures, reward functions, and training paradigms. We find that the use of policies with
arXiv:1903.01959v1
fatcat:2wudy6pgonhdpgfy6u4vjprryi