In this paper, we introduce the Action Schema Network (ASNet): a neural network architecture for learning generalised policies for probabilistic planning problems. By mimicking the relational structure of planning problems, ASNets are able to adopt a weight-sharing scheme which allows the network to be applied to any problem from a given planning domain. This allows the cost of training the network to be amortised over all problems in that domain. Further, we propose a training method which balances exploration and supervised training on small problems to produce a policy which remains robust when evaluated on larger problems. In experiments, we show that ASNet's learning capability allows it to significantly outperform traditional non-learning planners in several challenging domains.
@article{arxiv.1709.04271,
title = {Action Schema Networks: Generalised Policies with Deep Learning},
author = {Sam Toyer and Felipe Trevizan and Sylvie Thiébaux and Lexing Xie},
journal= {arXiv preprint arXiv:1709.04271},
year = {2017}
}