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arXiv · 2211.13051

Powderworld: A Platform for Understanding Generalization via Rich Task Distributions

Kevin Frans·Phillip Isola
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Abstract

One of the grand challenges of reinforcement learning is the ability to generalize to new tasks. However, general agents require a set of rich, diverse tasks to train on. Designing a `foundation environment' for such tasks is tricky -- the ideal environment would support a range of emergent phenomena, an expressive task space, and fast runtime. To take a step towards addressing this research bottleneck, this work presents Powderworld, a lightweight yet expressive simulation environment running directly on the GPU. Within Powderworld, two motivating challenges distributions are presented, one for world-modelling and one for reinforcement learning. Each contains hand-designed test tasks to examine generalization. Experiments indicate that increasing the environment's complexity improves generalization for world models and certain reinforcement learning agents, yet may inhibit learning in high-variance environments. Powderworld aims to support the study of generalization by providing a source of diverse tasks arising from the same core rules.

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Kevin Frans, Phillip Isola. 2022-11-23. Powderworld: A Platform for Understanding Generalization via Rich Task Distributions. https://arxiv.org/abs/2211.13051

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