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A framework for the emergence and analysis of language in social learning agents

Computer Science

A framework for the emergence and analysis of language in social learning agents

T. J. Wieczorek, T. Tchumatchenko, et al.

Explore how social learning agents communicate and improve performance with the innovative framework presented by Tobias J. Wieczorek and colleagues. This research highlights the effectiveness of language in encoding task information, showcasing its critical role in enhancing agent collaboration and task completion.

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Playback language: English
Abstract
This paper presents a framework for understanding the emergence and analysis of language in social learning agents using reinforcement learning in grid-world mazes. A teacher network transmits a message to a student network to improve task performance. The framework compresses high-dimensional task information into a low-dimensional representational space, mimicking natural language features. Results show that teacher information improves task completion and generalization, and optimizing message content enhances information encoding, highlighting language's role as a common representation among agents.
Publisher
Nature Communications
Published On
Aug 31, 2024
Authors
Tobias J. Wieczorek, Tatjana Tchumatchenko, Carlos Wert-Carvajal, Maximilian F. Eggl
Tags
social learning agents
language emergence
reinforcement learning
task performance
grid-world mazes
information encoding
teacher-student network

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