Heuristic Search Planning with Deep Neural Networks using Imitation, Attention and Curriculum Learning

• URL: http://arxiv.org/abs/2112.01918v1
• Date: Fri, 3 Dec 2021 14:01:16 GMT
• Title: Heuristic Search Planning with Deep Neural Networks using Imitation, Attention and Curriculum Learning
• Authors: Leah Chrestien, Tomas Pevny, Antonin Komenda, Stefan Edelkamp
• Abstract summary: This paper presents a network model to learn a capable of relating relating to distant parts of the state space via optimal plan imitation. To counter the limitation of the method in the creation of problems of increasing difficulty, we demonstrate the use of curriculum learning, where newly solved problem instances are added to the training set.
• Score: 1.0323063834827413
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Learning a well-informed heuristic function for hard task planning domains is an elusive problem. Although there are known neural network architectures to represent such heuristic knowledge, it is not obvious what concrete information is learned and whether techniques aimed at understanding the structure help in improving the quality of the heuristics. This paper presents a network model to learn a heuristic capable of relating distant parts of the state space via optimal plan imitation using the attention mechanism, which drastically improves the learning of a good heuristic function. To counter the limitation of the method in the creation of problems of increasing difficulty, we demonstrate the use of curriculum learning, where newly solved problem instances are added to the training set, which, in turn, helps to solve problems of higher complexities and far exceeds the performances of all existing baselines including classical planning heuristics. We demonstrate its effectiveness for grid-type PDDL domains.

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