A straighter internal map reportedly helped an AI planner reach maze goals more often
A post describing research from NYU, Brown and the University of Toronto says a rule that penalized bends in the model’s internal paths raised its goal-reaching rate from 52.7% to 90.7% in a two-room task.
TLDR
A user describes a world-model study that drew on neuroscience to make the model’s internal paths straighter. The post says its goal-reaching rate rose from 52.7% to 90.7% in a two-room task and from 44% to 94% in a U-shaped maze. With replanning, it reached 100% in both tests, according to the post. The tests were small, and the user says errors still add up over longer plans.
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