Reinforcement Learning-based Adaptive Path Selection for Programmable Networks
On the map: Deep reinforcement learning methods → RL for energy, networks and operations
Abstract (arXiv)
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.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in RL for energy, networks and operations, one of 15 directions in Deep reinforcement learning methods. Builds on 1 of its 15 references that are in the map. First posted on arXiv 19 Aug 2025.
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References · 1
- 2013Programming Protocol-Independent Packet Processorscited by 21
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