Evolving HyperNetworks for Game-Playing Agents

Christain Carvelli; Djordje Grbic; Sebastian Risi

doi:10.1145/3377929.3389961

Evolving HyperNetworks for Game-Playing Agents

Christain Carvelli, Djordje Grbic, Sebastian Risi

Research output: Conference Article in Proceeding or Book/Report chapter › Article in proceedings › Research › peer-review

Abstract

This work investigates the evolution of indirectly-encoded neural networks through a hypernetwork approach. We find that for some Atari games, a hypernetwork with over 14 times fewer parameters, can compete or even outperform directly-encoded policy networks. While hypernetworks perform worse than directly encoded networks in the game Frostbite, in the game Gravitar, the approach reaches a higher score than any other evolutionary method and outperforms complicated deep reinforcement learning setups such as Rainbow.

Original language	English
Title of host publication	GECCO '20: Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion
Number of pages	2
Publisher	Association for Computing Machinery
Publication date	8 Jul 2020
Pages	71-72
ISBN (Print)	9781450371278
DOIs	https://doi.org/10.1145/3377929.3389961
Publication status	Published - 8 Jul 2020
Event	GECCO 2020: The Genetic and Evolutionary Computation Conference - online, Cancun, Mexico Duration: 8 Jul 2020 → 12 Jul 2020 https://gecco-2020.sigevo.org/index.html/HomePage

Conference

Conference	GECCO 2020
Location	online
Country/Territory	Mexico
City	Cancun
Period	08/07/2020 → 12/07/2020
Internet address	https://gecco-2020.sigevo.org/index.html/HomePage

Keywords

Machine learning
Evolutionary algorithms
Artificial intelligence
Hypernetworks
Game-playing agents

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10.1145/3377929.3389961

carvelli_grbic_risi_2020Accepted author manuscript, 608 KB

Cite this

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title = "Evolving HyperNetworks for Game-Playing Agents",

abstract = "This work investigates the evolution of indirectly-encoded neural networks through a hypernetwork approach. We find that for some Atari games, a hypernetwork with over 14 times fewer parameters, can compete or even outperform directly-encoded policy networks. While hypernetworks perform worse than directly encoded networks in the game Frostbite, in the game Gravitar, the approach reaches a higher score than any other evolutionary method and outperforms complicated deep reinforcement learning setups such as Rainbow.",

keywords = "Machine learning, Evolutionary algorithms, Artificial intelligence, Hypernetworks, Game-playing agents, Machine learning, Evolutionary algorithms, Artificial intelligence, Hypernetworks, Game-playing agents",

author = "Christain Carvelli and Djordje Grbic and Sebastian Risi",

year = "2020",

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doi = "10.1145/3377929.3389961",

language = "English",

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booktitle = "GECCO '20: Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion",

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address = "United States",

note = "GECCO 2020 : The Genetic and Evolutionary Computation Conference, GECCO2020 ; Conference date: 08-07-2020 Through 12-07-2020",

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Evolving HyperNetworks for Game-Playing Agents. / Carvelli, Christain; Grbic, Djordje ; Risi, Sebastian.
GECCO '20: Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion. Association for Computing Machinery, 2020. p. 71-72.

Research output: Conference Article in Proceeding or Book/Report chapter › Article in proceedings › Research › peer-review

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KW - Machine learning

KW - Evolutionary algorithms

KW - Artificial intelligence

KW - Hypernetworks

KW - Game-playing agents

KW - Machine learning

KW - Evolutionary algorithms

KW - Artificial intelligence

KW - Hypernetworks

KW - Game-playing agents

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DO - 10.1145/3377929.3389961

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Evolving HyperNetworks for Game-Playing Agents

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Keywords

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