9 citations · 10 across the 7 of their papers we have counts for
8 papers
Learning How to Search for Plans with Exponentially Less Space
Dominik Drexler, Simon Ståhlberg, Markus Fritzsche +1
Heuristic search for a plan can store exponentially many states, even when its heuristic is almost perfect. We instead learn search control, one specification per domain, written a…
Learning General Policies with Policy Gradient Methods
Simon Ståhlberg, Blai Bonet, Hector Geffner
While reinforcement learning methods have delivered remarkable results in a number of settings, generalization, i.e., the ability to produce policies that generalize in a reliable…
First-Order Representation Languages for Goal-Conditioned RL
Simon Ståhlberg, Hector Geffner
First-order relational languages have been used in MDP planning and reinforcement learning (RL) for two main purposes: specifying MDPs in compact form, and representing and learnin…
Learning to Ground Existentially Quantified Goals
Martin Funkquist, Simon Ståhlberg, Hector Geffner
Goal instructions for autonomous AI agents cannot assume that objects have unique names. Instead, objects in goals must be referred to by providing suitable descriptions. However,…
Symmetries and Expressive Requirements for Learning General Policies
Dominik Drexler, Simon Ståhlberg, Blai Bonet +1
State symmetries play an important role in planning and generalized planning. In the first case, state symmetries can be used to reduce the size of the search; in the second, to re…
Learning More Expressive General Policies for Classical Planning Domains
Simon Ståhlberg, Blai Bonet, Hector Geffner
GNN-based approaches for learning general policies across planning domains are limited by the expressive power of , namely; first-order logic with two variables and counting.…
Learning Generalized Policies Without Supervision Using GNNs
Simon Ståhlberg, Blai Bonet, Hector Geffner
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 h…
Learning General Optimal Policies with Graph Neural Networks: Expressive Power, Transparency, and Limits
Simon Ståhlberg, Blai Bonet, Hector Geffner
It has been recently shown that general policies for many classical planning domains can be expressed and learned in terms of a pool of features defined from the domain predicates…