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DC Field | Value | Language |
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dc.contributor.author | Bontrager, Philip | - |
dc.contributor.author | Khalifa, Ahmed | - |
dc.contributor.author | Anderson, Damien | - |
dc.contributor.author | Stephenson, Matthew | - |
dc.contributor.author | Salge, Christoph | - |
dc.contributor.author | Togelius, Julian | - |
dc.date.accessioned | 2021-10-13T05:03:49Z | - |
dc.date.available | 2021-10-13T05:03:49Z | - |
dc.date.issued | 2019 | - |
dc.identifier.citation | Bontrager, P., Khalifa, A., Anderson, D., Stephenson, M., Salge, C., & Togelius, J. (2019). "Superstition" in the network : deep reinforcement learning plays deceptive games. Proceedings of the Fifteenth AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment (AIIDE-19), Atlanta. 10-16. | en_GB |
dc.identifier.issn | 23340924 | - |
dc.identifier.uri | https://www.um.edu.mt/library/oar/handle/123456789/82046 | - |
dc.description.abstract | Deep reinforcement learning has learned to play many games well, but failed on others. To better characterize the modes and reasons of failure of deep reinforcement learners, we test the widely used Asynchronous Actor-Critic (A2C) algorithm on four deceptive games, which are specially designed to provide challenges to game-playing agents. These games are implemented in the General Video Game AI framework, which allows us to compare the behavior of reinforcement learningbased agents with planning agents based on tree search. We find that several of these games reliably deceive deep reinforcement learners, and that the resulting behavior highlights the shortcomings of the learning algorithm. The particular ways in which agents fail differ from how planning-based agents fail, further illuminating the character of these algorithms. We propose an initial typology of deceptions which could help us better understand pitfalls and failure modes of (deep) reinforcement learning. | en_GB |
dc.language.iso | en | en_GB |
dc.publisher | Association for the Advancement of Artificial Intelligence (AAAI) | en_GB |
dc.rights | info:eu-repo/semantics/restrictedAccess | en_GB |
dc.subject | Computer games | en_GB |
dc.subject | Artificial intelligence | en_GB |
dc.subject | Machine learning | en_GB |
dc.subject | Reinforcement learning | en_GB |
dc.title | "Superstition" in the network : deep reinforcement learning plays deceptive games | en_GB |
dc.type | conferenceObject | en_GB |
dc.rights.holder | The copyright of this work belongs to the author(s)/publisher. The rights of this work are as defined by the appropriate Copyright Legislation or as modified by any successive legislation. Users may access this work and can make use of the information contained in accordance with the Copyright Legislation provided that the author must be properly acknowledged. Further distribution or reproduction in any format is prohibited without the prior permission of the copyright holder. | en_GB |
dc.bibliographicCitation.conferencename | Fifteenth AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment 2019 | en_GB |
dc.bibliographicCitation.conferenceplace | Atlanta, United States, 08-12/10/2019 | en_GB |
dc.description.reviewed | peer-reviewed | en_GB |
Appears in Collections: | Scholarly Works - InsDG |
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File | Description | Size | Format | |
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Superstition_in_the_network_deep_reinforcement_learning_plays_deceptive_games_2019.pdf Restricted Access | 1.11 MB | Adobe PDF | View/Open Request a copy |
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