A Multi-Agent, Policy-Gradient approach to Network Routing
Fuente:
arXiv
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| Hauptverfasser: | , , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| _version_ | 1866912745686827008 |
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| author | Tao, Nigel Baxter, Jonathan Weaver, Lex |
| author_facet | Tao, Nigel Baxter, Jonathan Weaver, Lex |
| contents | Network routing is a distributed decision problem which naturally admits numerical performance measures, such as the average time for a packet to travel from source to destination. OLPOMDP, a policy-gradient reinforcement learning algorithm, was successfully applied to simulated network routing under a number of network models. Multiple distributed agents (routers) learned co-operative behavior without explicit inter-agent communication, and they avoided behavior which was individually desirable, but detrimental to the group's overall performance. Furthermore, shaping the reward signal by explicitly penalizing certain patterns of sub-optimal behavior was found to dramatically improve the convergence rate. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_03211 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Multi-Agent, Policy-Gradient approach to Network Routing Tao, Nigel Baxter, Jonathan Weaver, Lex Machine Learning Networking and Internet Architecture Network routing is a distributed decision problem which naturally admits numerical performance measures, such as the average time for a packet to travel from source to destination. OLPOMDP, a policy-gradient reinforcement learning algorithm, was successfully applied to simulated network routing under a number of network models. Multiple distributed agents (routers) learned co-operative behavior without explicit inter-agent communication, and they avoided behavior which was individually desirable, but detrimental to the group's overall performance. Furthermore, shaping the reward signal by explicitly penalizing certain patterns of sub-optimal behavior was found to dramatically improve the convergence rate. |
| title | A Multi-Agent, Policy-Gradient approach to Network Routing |
| topic | Machine Learning Networking and Internet Architecture |
| url | https://arxiv.org/abs/2512.03211 |