Mastering percolation-like games with deep learning

Michael M. Danziger

doi:10.1103/PhysRevResearch.6.013067

Mastering percolation-like games with deep learning

2024-01-17

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DOI：10.1103/PhysRevResearch.6.013067

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Though robustness of networks to random attacks has been widely studied, intentional destruction by an intelligent agent is not tractable with previous methods. Here we devise a single-player game on a lattice that mimics the logic of an attacker attempting to destroy a network. The objective of the game is to disable all nodes in the fewest number of steps. We develop a reinforcement learning approach using deep Q-learning that is capable of learning to play this game successfully, and in so doing, to optimally attack a network. Because the learning algorithm is universal, we train agents on different definitions of robustness and compare the learned strategies. We find that superficially similar definitions of robustness induce different strategies in the trained agent, implying that optimally attacking or defending a network is sensitive to the particular objective. Our method provides an approach to understand network robustness, with potential applications to other discrete processes in disordered systems.

Keywords:

FRACTAL DIMENSION

CLUSTER

NETWORK

INTERNET

GO

Mastering percolation-like games with deep learning

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