Related papers: Puzzle Solving without Search or Human Knowledge: An Unnatural Language Approach

Puzzle Solving without Search or Human Knowledge: An Unnatural Language Approach

URL: http://arxiv.org/abs/2109.02797v1
Date: Tue, 7 Sep 2021 01:20:28 GMT
Title: Puzzle Solving without Search or Human Knowledge: An Unnatural Language Approach
Authors: David Noever and Ryerson Burdick
Abstract summary: The application of Generative Pre-trained Transformer (GPT-2) to learn text-archived game notation provides a model environment for exploring sparse reward gameplay. The transformer architecture proves amenable to training on solved text archives describing mazes, Rubik's, and Sudoku solvers.
Score: 0.0
License: http://creativecommons.org/licenses/by/4.0/
Abstract: The application of Generative Pre-trained Transformer (GPT-2) to learn text-archived game notation provides a model environment for exploring sparse reward gameplay. The transformer architecture proves amenable to training on solved text archives describing mazes, Rubik's Cube, and Sudoku solvers. The method benefits from fine-tuning the transformer architecture to visualize plausible strategies derived outside any guidance from human heuristics or domain expertise. The large search space ($>10^{19}$) for the games provides a puzzle environment in which the solution has few intermediate rewards and a final move that solves the challenge.

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