{"ID":2880681,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2508.13806","arxiv_id":"2508.13806","title":"Reinforcement Learning-based Adaptive Path Selection for Programmable Networks","abstract":"This work presents a proof-of-concept implementation of a distributed, in-network reinforcement learning (IN-RL) framework for adaptive path selection in programmable networks. By combining Stochastic Learning Automata (SLA) with real-time telemetry data collected via In-Band Network Telemetry (INT), the proposed system enables local, data-driven forwarding decisions that adapt dynamically to congestion conditions. The system is evaluated on a Mininet-based testbed using P4-programmable BMv2 switches, demonstrating how our SLA-based mechanism converges to effective path selections and adapts to shifting network conditions at line rate.","short_abstract":"This work presents a proof-of-concept implementation of a distributed, in-network reinforcement learning (IN-RL) framework for adaptive path selection in programmable networks. By combining Stochastic Learning Automata (SLA) with real-time telemetry data collected via In-Band Network Telemetry (INT), the proposed syste...","url_abs":"https://arxiv.org/abs/2508.13806","url_pdf":"https://arxiv.org/pdf/2508.13806v2","authors":"[\"José Eduardo Zerna Torres\",\"Marios Avgeris\",\"Chrysa Papagianni\",\"Gergely Pongrácz\",\"István Gódor\",\"Paola Grosso\"]","published":"2025-08-19T13:12:17Z","proceeding":"cs.LG","tasks":"[\"cs.LG\"]","methods":"[\"Reinforcement Learning\"]","has_code":false}