A Model of External Memory for Navigation in Partially Observative Visual Reinforcement Learning Tasks

Created by W.Langdon from gp-bibliography.bib Revision:1.7615

@InProceedings{Smith:2019:EuroGP,

• author = "Robert Smith and Malcolm Heywood",
• title = "A Model of External Memory for Navigation in Partially Observative Visual Reinforcement Learning Tasks",
• booktitle = "EuroGP 2019: Proceedings of the 22nd European Conference on Genetic Programming",
• year = "2019",
• month = "24-26 " # apr,
• editor = "Lukas Sekanina and Ting Hu and Nuno Lourenco",
• series = "LNCS",
• volume = "11451",
• publisher = "Springer Verlag",
• address = "Leipzig, Germany",
• pages = "162--177",
• organisation = "EvoStar, Species",
• keywords = "genetic algorithms, genetic programming, Computer Video Games, First person shooter",
• isbn13 = "978-3-030-16669-4",
• URL = "https://www.springer.com/us/book/9783030166694",
• DOI = "doi:10.1007/978-3-030-16670-0_11",
• size = "16 pages",
• abstract = "Visual reinforcement learning implies that, decision making policies are identified under delayed rewards from an environment. Moreover, state information takes the form of high-dimensional data, such as video. In addition, although the video might characterize a 3D world in high resolution, partial observability will place significant limits on what the agent can actually perceive of the world. This means that the agent also has to: (1) provide efficient encodings of state, (2) store the encodings of state efficiently in some form of memory, (3) recall such memories after arbitrary delays for decision making. In this work, we demonstrate how an external memory model facilitates decision making in the complex world of multi-agent deathmatches in the ViZDoom first person shooter environment. The ViZDoom environment provides a complex environment of multiple rooms and resources in which agents are spawned from multiple different locations. A unique approach is adopted to defining external memory for genetic programming agents in which: (1) the state of memory is shared across all programs. (2) Writing is formulated as a probabilistic process, resulting in different regions of memory having short- versus long-term memory. (3) Read operations are indexed, enabling programs to identify regions of external memory with specific temporal properties. We demonstrate that agents purposefully navigate the world when external memory is provided, whereas those without external memory are limited to merely flight or fight behaviour.",
• notes = "http://www.evostar.org/2019/cfp_eurogp.php#abstracts Part of \cite{Sekanina:2019:GP} EuroGP'2019 held in conjunction with EvoCOP2019, EvoMusArt2019 and EvoApplications2019",

}

Genetic Programming entries for Robert J Smith Malcolm Heywood

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