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Main Author: Bhardwaj, Mannan
Format: Preprint
Published: 2025
Subjects:
Online Access:https://arxiv.org/abs/2505.02215
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author Bhardwaj, Mannan
author_facet Bhardwaj, Mannan
contents This paper explores the emergence of language in multi-agent reinforcement learning (MARL) using transformers. Existing methods such as RIAL, DIAL, and CommNet enable agent communication but lack interpretability. We propose Differentiable Inter-Agent Transformers (DIAT), which leverage self-attention to learn symbolic, human-understandable communication protocols. Through experiments, DIAT demonstrates the ability to encode observations into interpretable vocabularies and meaningful embeddings, effectively solving cooperative tasks. These results highlight the potential of DIAT for interpretable communication in complex multi-agent environments.
format Preprint
id arxiv_https___arxiv_org_abs_2505_02215
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle Interpretable Emergent Language Using Inter-Agent Transformers
Bhardwaj, Mannan
Artificial Intelligence
Computation and Language
This paper explores the emergence of language in multi-agent reinforcement learning (MARL) using transformers. Existing methods such as RIAL, DIAL, and CommNet enable agent communication but lack interpretability. We propose Differentiable Inter-Agent Transformers (DIAT), which leverage self-attention to learn symbolic, human-understandable communication protocols. Through experiments, DIAT demonstrates the ability to encode observations into interpretable vocabularies and meaningful embeddings, effectively solving cooperative tasks. These results highlight the potential of DIAT for interpretable communication in complex multi-agent environments.
title Interpretable Emergent Language Using Inter-Agent Transformers
topic Artificial Intelligence
Computation and Language
url https://arxiv.org/abs/2505.02215