Evolutionary Development of Hierarchical Learning Structures

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@Article{Elfwing:2007:tec,

• author = "Stefan Elfwing and Eiji Uchibe and Kenji Doya and Henrik I. Christensen",
• title = "Evolutionary Development of Hierarchical Learning Structures",
• journal = "IEEE Transactions on Evolutionary Computation",
• year = "2007",
• volume = "11",
• number = "2",
• pages = "249--264",
• month = apr,
• keywords = "genetic algorithms, genetic programming, learning (artificial intelligence), Lamarckian evolutionary development, MAXQ hierarchical RL method, foraging task, genetic programming, hierarchical learning structures, hierarchical reinforcement learning, task decomposition",
• DOI = "doi:10.1109/TEVC.2006.890270",
• ISSN = "1089-778X",
• abstract = "Hierarchical reinforcement learning (RL) algorithms can learn a policy faster than standard RL algorithms. However, the applicability of hierarchical RL algorithms is limited by the fact that the task decomposition has to be performed in advance by the human designer. We propose a Lamarckian evolutionary approach for automatic development of the learning structure in hierarchical RL. The proposed method combines the MAXQ hierarchical RL method and genetic programming (GP). In the MAXQ framework, a subtask can optimise the policy independently of its parent task's policy, which makes it possible to reuse learned policies of the subtasks. In the proposed method, the MAXQ method learns the policy based on the task hierarchies obtained by GP, while the GP explores the appropriate hierarchies using the result of the MAXQ method. To show the validity of the proposed method, we have performed simulation experiments for a foraging task in three different environmental settings. The results show strong interconnection between the obtained learning structures and the given task environments. The main conclusion of the experiments is that the GP can find a minimal strategy, i.e., a hierarchy that minimises the number of primitive subtasks that can be executed for each type of situation. The experimental results for the most challenging environment also show that the policies of the subtasks can continue to improve, even after the structure of the hierarchy has been evolutionary stabilised, as an effect of Lamarckian mechanisms",

}

Genetic Programming entries for Stefan Elfwing Eiji Uchibe Kenji Doya Henrik I Christensen

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