Reinforcement learning and artificial agency

被引:4
|
作者
Butlin, Patrick [1 ,2 ]
机构
[1] Univ Oxford, Future Humanity Inst, Oxford, Oxfordshire, England
[2] Univ Oxford, Future Humanity Inst, Trajan House,Mill St, Oxford OX2 0DJ, Oxfordshire, England
关键词
action for reasons; agency; artificial intelligence; minimal agency; reinforcement learning; ORGANIZATIONAL ACCOUNT; GO; DISPOSITIONS; ANIMALS; REASON; SHOGI; CHESS;
D O I
10.1111/mila.12458
中图分类号
H0 [语言学];
学科分类号
030303 ; 0501 ; 050102 ;
摘要
There is an apparent connection between reinforcement learning and agency. Artificial entities controlled by reinforcement learning algorithms are standardly referred to as agents, and the mainstream view in the psychology and neuroscience of agency is that humans and other animals are reinforcement learners. This article examines this connection, focusing on artificial reinforcement learning systems and assuming that there are various forms of agency. Artificial reinforcement learning systems satisfy plausible conditions for minimal agency, and those which use models of the environment to perform forward search are capable of a form of agency which may reasonably be called action for reasons.
引用
收藏
页码:22 / 38
页数:17
相关论文
共 50 条
  • [1] Explainable Agency in Reinforcement Learning Agents
    Madumal, Prashan
    THIRTY-FOURTH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE, THE THIRTY-SECOND INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE AND THE TENTH AAAI SYMPOSIUM ON EDUCATIONAL ADVANCES IN ARTIFICIAL INTELLIGENCE, 2020, 34 : 13724 - 13725
  • [2] Agency, learning and animal-based reinforcement learning
    Alonso, E
    Mondragón, E
    AGENTS AND COMPUTATIONAL AUTONOMY: POTENTIAL, RISKS, AND SOLUTIONS, 2004, 2969 : 1 - 6
  • [3] Reinforcement learning with artificial microswimmers
    Muiños-Landin S.
    Fischer A.
    Holubec V.
    Cichos F.
    Science Robotics, 2020, 6 (52):
  • [4] Reinforcement learning with artificial microswimmers
    Muinos-Landin, S.
    Fischer, A.
    Holubec, V.
    Cichos, F.
    SCIENCE ROBOTICS, 2021, 6 (52)
  • [5] Reinforcement learning in artificial and biological systems
    Emre O. Neftci
    Bruno B. Averbeck
    Nature Machine Intelligence, 2019, 1 : 133 - 143
  • [6] Reinforcement learning in artificial and biological systems
    Neftci, Emre O.
    Averbeck, Bruno B.
    NATURE MACHINE INTELLIGENCE, 2019, 1 (03) : 133 - 143
  • [7] Deep Reinforcement Learning with Artificial Microswimmers
    Pradip, Ravi
    Cichos, Frank
    EMERGING TOPICS IN ARTIFICIAL INTELLIGENCE (ETAI) 2022, 2022, 12204
  • [8] Speeding up Reinforcement Learning by Combining Attention and Agency Features
    Demirel, Berkay
    Sanchez-Fibla, Marti
    ARTIFICIAL INTELLIGENCE RESEARCH AND DEVELOPMENT, 2019, 319 : 84 - 94
  • [9] From reinforcement learning to agency: Frameworks for understanding basal cognition
    Seifert, Gabriella
    Sealander, Ava
    Marzen, Sarah
    Levin, Michael
    BIOSYSTEMS, 2024, 235
  • [10] Reinforcement Learning Based Artificial Immune Classifier
    Karakose, Mehmet
    SCIENTIFIC WORLD JOURNAL, 2013,