Fully-Decentralized MADDPG with Networked Agents
Fuente:
arXiv
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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Acceso en línea: | |
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| _version_ | 1866916647946682368 |
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| author | Bolliger, Diego Zauter, Lorenz Ziegler, Robert |
| author_facet | Bolliger, Diego Zauter, Lorenz Ziegler, Robert |
| contents | In this paper, we devise three actor-critic algorithms with decentralized training for multi-agent reinforcement learning in cooperative, adversarial, and mixed settings with continuous action spaces. To this goal, we adapt the MADDPG algorithm by applying a networked communication approach between agents. We introduce surrogate policies in order to decentralize the training while allowing for local communication during training. The decentralized algorithms achieve comparable results to the original MADDPG in empirical tests, while reducing computational cost. This is more pronounced with larger numbers of agents. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_06747 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Fully-Decentralized MADDPG with Networked Agents Bolliger, Diego Zauter, Lorenz Ziegler, Robert Machine Learning Artificial Intelligence Multiagent Systems In this paper, we devise three actor-critic algorithms with decentralized training for multi-agent reinforcement learning in cooperative, adversarial, and mixed settings with continuous action spaces. To this goal, we adapt the MADDPG algorithm by applying a networked communication approach between agents. We introduce surrogate policies in order to decentralize the training while allowing for local communication during training. The decentralized algorithms achieve comparable results to the original MADDPG in empirical tests, while reducing computational cost. This is more pronounced with larger numbers of agents. |
| title | Fully-Decentralized MADDPG with Networked Agents |
| topic | Machine Learning Artificial Intelligence Multiagent Systems |
| url | https://arxiv.org/abs/2503.06747 |