  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.
