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It is in the abstract, very first line. "While Transformers have enabled tremendous progress in various application settings, such architectures still lag behin
by RogerL 3y ago
It is in the abstract, very first line. "While Transformers have enabled tremendous progress in various application settings, such architectures still lag behind traditional symbolic planners for solving complex decision making tasks. In this work, we demonstrate how to train Transformers to solve complex planning tasks ..."
This paper is interesting (to me) as an example of how to use transformers for decision making. I don't particularly care if it is up to A* standards at the moment.
- tintor 3y agoAbstract doesn't answer my question. What is the scientific contribution of the paper? They trained transformer on pairs of <sokoban_level, A*_optimal_solution>.
- gopher_space 3y ago> What is the scientific contribution of the paper? That's not a question you ask other people, that's a bullet point at the top of the outline you created while reading the paper for yourself. You should see the creation of said outline as a measure of your actual interest in the subject.
- jsemrau 3y agoHaving now read the paper, the research area is interesting because optimizations in optimal path finding have applications in robotics, gaming, and reasoning (what the ultimate intention of this paper is). The research team identified ways to tokenized path finding algorithms for two tasks maze solving and sokoban (a game where a crate has to be pushed to goal) and then trained a model on the execution traces of these algorithms. The insight this provided was that the "searchformer" model was about 26% faster than the traditional methods. If that is applied to Route-planning, Robotics, and Game Development, it could have tangible performance benefits. IMHO, it is not a wild breakthrough but an interesting solution to a real-world problem.