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arXiv · 2411.01342

Adaptive World Models: Learning Behaviors by Latent Imagination Under Non-Stationarity

Emiliyan Gospodinov·Vaisakh Shaj·Philipp Becker·Stefan Geyer·Gerhard Neumann
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Abstract

Developing foundational world models is a key research direction for embodied intelligence, with the ability to adapt to non-stationary environments being a crucial criterion. In this work, we introduce a new formalism, Hidden Parameter-POMDP, designed for control with adaptive world models. We demonstrate that this approach enables learning robust behaviors across a variety of non-stationary RL benchmarks. Additionally, this formalism effectively learns task abstractions in an unsupervised manner, resulting in structured, task-aware latent spaces.

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Emiliyan Gospodinov, Vaisakh Shaj, Philipp Becker, Stefan Geyer, Gerhard Neumann. 2024-11-02. Adaptive World Models: Learning Behaviors by Latent Imagination Under Non-Stationarity. https://arxiv.org/abs/2411.01342

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