Active World Model Learning with Progress Curiosity
This work addresses the challenge of efficient exploration for learning world models, which is incremental as it builds on existing curiosity methods.
The paper tackles the problem of curiosity-driven active world model learning in 3D environments by proposing a $\\gamma$-Progress signal, which overcomes the 'white noise problem' and achieves significantly higher performance than state-of-the-art baselines like Random Network Distillation and Model Disagreement.
World models are self-supervised predictive models of 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 construct a curious agent building world models while visually exploring a 3D physical environment rich with distillations of representative real-world agents. We propose an AWML system driven by $γ$-Progress: a scalable and effective learning progress-based curiosity signal. We show that $γ$-Progress naturally gives rise to an exploration policy that directs attention to complex but learnable dynamics in a balanced manner, thus overcoming the "white noise problem". 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.