See, Hear, Explore: Curiosity via Audio-Visual Association

• URL: http://arxiv.org/abs/2007.03669v2
• Date: Mon, 18 Jan 2021 16:26:54 GMT
• Title: See, Hear, Explore: Curiosity via Audio-Visual Association
• Authors: Victoria Dean, Shubham Tulsiani, Abhinav Gupta
• Abstract summary: A common formulation of curiosity-driven exploration uses the difference between the real future and the future predicted by a learned model. In this paper, we introduce an alternative form of curiosity that rewards novel associations between different senses. Our approach exploits multiple modalities to provide a stronger signal for more efficient exploration.
• Score: 46.86865495827888
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Exploration is one of the core challenges in reinforcement learning. A common formulation of curiosity-driven exploration uses the difference between the real future and the future predicted by a learned model. However, predicting the future is an inherently difficult task which can be ill-posed in the face of stochasticity. In this paper, we introduce an alternative form of curiosity that rewards novel associations between different senses. Our approach exploits multiple modalities to provide a stronger signal for more efficient exploration. Our method is inspired by the fact that, for humans, both sight and sound play a critical role in exploration. We present results on several Atari environments and Habitat (a photorealistic navigation simulator), showing the benefits of using an audio-visual association model for intrinsically guiding learning agents in the absence of external rewards. For videos and code, see https://vdean.github.io/audio-curiosity.html.

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