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Reinforcement with Iterative Punishment

Barrett, Jeffrey A. and Gabriel, Nathan (2021) Reinforcement with Iterative Punishment. [Preprint]

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Abstract

We consider the efficacy of various forms of reinforcement learning with punishment in evolving linguistic conventions in the context of Lewis-Skyrms signaling games. We show that the learning strategy of reinforcement with iterative punishment is highly effective at evolving optimal conventions in even complex signaling games. It is also robust and can be easily extended to a self-tuning variety of reinforcement learning. We briefly discuss some of the virtues of reinforcement with iterative punishment and how it may be related to learning in nature.


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Item Type: Preprint
Creators:
CreatorsEmailORCID
Barrett, Jeffrey A.j.barrett@uci.edu
Gabriel, Nathanngabrie2@uci.edu
Keywords: reinforcement learning, Lewis-Skyrms signaling games, evolution of language
Subjects: Specific Sciences > Artificial Intelligence > Classical AI
Specific Sciences > Computation/Information
Specific Sciences > Computer Science
General Issues > Computer Simulation
General Issues > Formal Learning Theory
General Issues > Game Theory
Specific Sciences > Artificial Intelligence > Machine Learning
Depositing User: Jeffrey Barrett
Date Deposited: 11 Sep 2021 15:20
Last Modified: 11 Sep 2021 15:20
Item ID: 19546
Subjects: Specific Sciences > Artificial Intelligence > Classical AI
Specific Sciences > Computation/Information
Specific Sciences > Computer Science
General Issues > Computer Simulation
General Issues > Formal Learning Theory
General Issues > Game Theory
Specific Sciences > Artificial Intelligence > Machine Learning
Date: 8 September 2021
URI: https://philsci-archive.pitt.edu/id/eprint/19546

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