70 citations · 249 across the 13 of their papers we have counts for
13 papers
ASNets: Deep Learning for Generalised Planning
Sam Toyer, Felipe Trevizan, Sylvie Thiébaux +1
In this paper, we discuss the learning of generalised policies for probabilistic and classical planning problems using Action Schema Networks (ASNets). The ASNet is a neural networ…
Reward Potentials for Planning with Learned Neural Network Transition Models
Buser Say, Scott Sanner, Sylvie Thiébaux
Optimal planning with respect to learned neural network (NN) models in continuous action and state spaces using mixed-integer linear programming (MILP) is a challenging task for br…
Action Schema Networks: Generalised Policies with Deep Learning
Sam Toyer, Felipe Trevizan, Sylvie Thiébaux +1
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…
Tableaux for Policy Synthesis for MDPs with PCTL* Constraints
Peter Baumgartner, Sylvie Thiébaux, Felipe Trevizan
Markov decision processes (MDPs) are the standard formalism for modelling sequential decision making in stochastic environments. Policy synthesis addresses the problem of how to co…
Numerical Integration and Dynamic Discretization in Heuristic Search Planning over Hybrid Domains
Miquel Ramirez, Enrico Scala, Patrik Haslum +1
In this paper we look into the problem of planning over hybrid domains, where change can be both discrete and instantaneous, or continuous over time. In addition, it is required th…
Dynamic Optimal Power Flow in Microgrids using the Alternating Direction Method of Multipliers
Paul Scott, Sylvie Thiébaux
Smart devices, storage and other distributed technologies have the potential to greatly improve the utilisation of network infrastructure and renewable generation. Decentralised co…