90 citations · 351 across the 32 of their papers we have counts for
37 papers
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…
Learning General Policies From Examples
Blai Bonet, Hector Geffner
Combinatorial methods for learning general policies that solve large collections of planning problems have been recently developed. One of their strengths, in relation to deep lear…
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 Generalized Policies for Fully Observable Non-Deterministic Planning Domains
Till Hofmann, Hector Geffner
General policies represent reactive strategies for solving large families of planning problems like the infinite collection of solvable instances from a given domain. Methods for l…
On Policy Reuse: An Expressive Language for Representing and Executing General Policies that Call Other Policies
Blai Bonet, Dominik Drexler, Hector Geffner
Recently, a simple but powerful language for expressing and learning general policies and problem decompositions (sketches) has been introduced in terms of rules defined over a set…