Active World Model Learning with Progress Curiosity

Jul 12, 2020

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World models are a family of predictive models that solve self-supervised problems on how the world evolves. Humans learn world models by curiously exploring their environment, in the process acquiring compact abstractions of high bandwidth sensory inputs, the ability to plan across long temporal horizons, and an understanding of the behavioral patterns of other agents. In this work, we study how to design such a curiosity-driven Active World Model Learning (AWML) system. To do so, we simulate a curious agent building world models while visually exploring a 3D physical environment rich with distillations of representative real-world stimuli. We propose an AWML system driven by γ-Progress: a scalable and effective learning progress-based curiosity signal. We show that γ-Progress is robust to "white noise" and naturally gives rise to an exploration policy that allocates attention in a balanced manner, with a preference towards agents displaying complex yet learnable behaviors. As a result, our γ-Progress driven controller achieves significantly higher AWML performance than baseline controllers equipped with state-of-the-art exploration strategies such as Random Network Distillation and Model Disagreement.

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About ICML 2020

The International Conference on Machine Learning (ICML) is the premier gathering of professionals dedicated to the advancement of the branch of artificial intelligence known as machine learning. ICML is globally renowned for presenting and publishing cutting-edge research on all aspects of machine learning used in closely related areas like artificial intelligence, statistics and data science, as well as important application areas such as machine vision, computational biology, speech recognition, and robotics. ICML is one of the fastest growing artificial intelligence conferences in the world. Participants at ICML span a wide range of backgrounds, from academic and industrial researchers, to entrepreneurs and engineers, to graduate students and postdocs.

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