Achieving Optimal Tissue Repair Through MARL with Reward Shaping and Curriculum Learning
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arXiv
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| Autores principales: | , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866915243389616128 |
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| author | Khan, Muhammad Al-Zafar Al-Karaki, Jamal |
| author_facet | Khan, Muhammad Al-Zafar Al-Karaki, Jamal |
| contents | In this paper, we present a multi-agent reinforcement learning (MARL) framework for optimizing tissue repair processes using engineered biological agents. Our approach integrates: (1) stochastic reaction-diffusion systems modeling molecular signaling, (2) neural-like electrochemical communication with Hebbian plasticity, and (3) a biologically informed reward function combining chemical gradient tracking, neural synchronization, and robust penalties. A curriculum learning scheme guides the agent through progressively complex repair scenarios. In silico experiments demonstrate emergent repair strategies, including dynamic secretion control and spatial coordination. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_10677 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Achieving Optimal Tissue Repair Through MARL with Reward Shaping and Curriculum Learning Khan, Muhammad Al-Zafar Al-Karaki, Jamal Machine Learning Artificial Intelligence Multiagent Systems In this paper, we present a multi-agent reinforcement learning (MARL) framework for optimizing tissue repair processes using engineered biological agents. Our approach integrates: (1) stochastic reaction-diffusion systems modeling molecular signaling, (2) neural-like electrochemical communication with Hebbian plasticity, and (3) a biologically informed reward function combining chemical gradient tracking, neural synchronization, and robust penalties. A curriculum learning scheme guides the agent through progressively complex repair scenarios. In silico experiments demonstrate emergent repair strategies, including dynamic secretion control and spatial coordination. |
| title | Achieving Optimal Tissue Repair Through MARL with Reward Shaping and Curriculum Learning |
| topic | Machine Learning Artificial Intelligence Multiagent Systems |
| url | https://arxiv.org/abs/2504.10677 |