12 citations · 12 across the 4 of their papers we have counts for
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cs.AI2019
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…
cs.AI2019
Scalable Planning with Deep Neural Network Learned Transition Models
Ga Wu, Buser Say, Scott Sanner
In many real-world planning problems with factored, mixed discrete and continuous state and action spaces such as Reservoir Control, Heating Ventilation, and Air Conditioning, and…