Search Results for author:
Found 3 papers, 1 papers with code
Learning First-Order Representations for Planning from Black-Box States: New Results
For this, the learning problem is formulated as the search for a simplest first-order domain description D that along with information about instances I_i (number of objects and initial state) determine state space graphs G(P_i) that match the observed state graphs G_i where P_i = (D, I_i).
Flexible FOND Planning with Explicit Fairness Assumptions
The infinite trajectories that violate this condition are deemed as unfair, and the solutions are policies for which all the fair trajectories reach a goal state.
Neural-Network Quantum States, String-Bond States, and Chiral Topological States
In particular we demonstrate that short-range Restricted Boltzmann Machines are Entangled Plaquette States, while fully connected Restricted Boltzmann Machines are String-Bond States with a nonlocal geometry and low bond dimension.
