Keywords: reinforcement learning, environment, generalization, out-of-distribution, multi-task
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 task 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, yet causes reinforcement learning agents to struggle. 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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TL;DR: Powderworld is an environment supporting the study of generalization by providing diverse tasks arising from the same core rules.
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