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arXiv · 2207.08021

Role of reward shaping in object-goal navigation

Srirangan Madhavan·Anwesan Pal·Henrik I. Christensen
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Abstract

Deep reinforcement learning approaches have been a popular method for visual navigation tasks in the computer vision and robotics community of late. In most cases, the reward function has a binary structure, i.e., a large positive reward is provided when the agent reaches goal state, and a negative step penalty is assigned for every other state in the environment. A sparse signal like this makes the learning process challenging, specially in big environments, where a large number of sequential actions need to be taken to reach the target. We introduce a reward shaping mechanism which gradually adjusts the reward signal based on distance to the goal. Detailed experiments conducted using the AI2-THOR simulation environment demonstrate the efficacy of the proposed approach for object-goal navigation tasks.

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BibTeXRIS

Srirangan Madhavan, Anwesan Pal, Henrik I. Christensen. 2022-07-16. Role of reward shaping in object-goal navigation. https://arxiv.org/abs/2207.08021

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