Web: http://arxiv.org/abs/2205.06002

May 13, 2022, 1:11 a.m. | Simon Ståhlberg, Blai Bonet, Hector Geffner

cs.LG updates on arXiv.org arxiv.org

We consider the problem of learning generalized policies for classical
planning domains using graph neural networks from small instances represented
in lifted STRIPS. The problem has been considered before but the proposed
neural architectures are complex and the results are often mixed. In this work,
we use a simple and general GNN architecture and aim at obtaining crisp
experimental results and a deeper understanding: either the policy greedy in
the learned value function achieves close to 100% generalization over instances …

ai arxiv gnns learning

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