Emergent Policy Discovery for Visual Reinforcement Learning Through Tangled Program Graphs: A Tutorial

Created by W.Langdon from gp-bibliography.bib Revision:1.7630

@InProceedings{kelly:2018:GPTP,

• author = "Stephen Kelly and Robert J. Smith and Malcolm I. Heywood",
• title = "Emergent Policy Discovery for Visual Reinforcement Learning Through Tangled Program Graphs: A Tutorial",
• booktitle = "Genetic Programming Theory and Practice XVI",
• year = "2018",
• editor = "Wolfgang Banzhaf and Lee Spector and Leigh Sheneman",
• pages = "37--57",
• address = "Ann Arbor, USA",
• month = "17-20 " # may,
• publisher = "Springer",
• keywords = "genetic algorithms, genetic programming",
• isbn13 = "978-3-030-04734-4",
• URL = "http://link.springer.com/chapter/10.1007/978-3-030-04735-1_3",
• DOI = "doi:10.1007/978-3-030-04735-1_3",
• abstract = "Tangled Program Graphs (TPG) represents a framework by which multiple programs can be organized to cooperate and decompose a task with minimal a priori information. TPG agents begin with least complexity and incrementally coevolve to discover a complexity befitting the nature of the task. Previous research has demonstrated the TPG framework under visual reinforcement learning tasks from the Arcade Learning Environment and VizDoom first person shooter game that are competitive with those from Deep Learning. However, unlike Deep Learning the emergent constructive properties of TPG results in solutions that are orders of magnitude simpler, thus execution never needs hardware support. In this work, our goal is to provide a tutorial overview demonstrating how the emergent properties of TPG have been achieved as well as providing specific examples of decompositions discovered under the VizDoom task.",

}

Genetic Programming entries for Stephen Kelly Robert J Smith Malcolm Heywood

Citations