Noisy Agents: Self-supervised Exploration by Predicting Auditory Events
Chuang Gan,Xiaoyu Chen,Phillip Isola,Antonio Torralba,Joshua B. Tenenbaum,Chuang Gan,Xiaoyu Chen,Phillip Isola,Antonio Torralba,Joshua B. Tenenbaum
Humans integrate multiple sensory modalities (e.g., visual and audio) to build a causal understanding of the physical world. In this work, we propose a novel type of intrinsic motivation for Reinforcement Learning (RL) that encourages the agent to understand the causal effect of its actions through auditory event prediction. First, we allow the agent to collect a small amount of acoustic data and ...