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Logiciel Année : 2022

Packet Routing Simulator for Multi-Agent Reinforcement Learning (PRISMA) (Version v0.1)

Résumé

PRISMA (Packet Routing Simulator for Multi-Agent Reinforcement Learning) is a network simulation playground for developing and testing Multi-Agent Reinforcement Learning (MARL) solutions for dynamic packet routing (DPR). This framework is based on the OpenAI Gym toolkit and the ns-3 simulator. The OpenAI Gym is a toolkit for RL widely used in research. The network simulator ns–3 is a standard library, which may provide useful simulation tools. It generates discrete events and provides several protocol implementations. Moreover, the NetSim implementation is based on ns3-gym, which integrates OpenAI Gym and ns-3. The main contributions of this framework: 1) A RL framework designed for specifically the DPR problem, serving as a playground where the community can easily validate their own RL approaches and compare them. 2) A more realistic modelling based on: (i) the well-known ns-3 network simulator, and (ii) a multi-threaded implementation for each agent. 3) A modular code design, which allows a researcher to test their own RL algorithm for the DPR problem, without needing to work on the implementation of the environment.

Citer

Redha A. Alliche, Tiago da Silva Barros, Ramon Aparicio-Pardo, Lucile Sassatelli. Packet Routing Simulator for Multi-Agent Reinforcement Learning (PRISMA) (Version v0.1). 2022, ⟨swh:1:dir:53e77bdd5593e2ef85805c8520626cfddb113fa9;origin=https://hal.archives-ouvertes.fr/hal-03998842;visit=swh:1:snp:c4feb2a2b2254e41f7187021d2905e65d12d07a6;anchor=swh:1:rel:29f812793e523375296b6ff52ffc50ce68250a98;path=/⟩. ⟨hal-03998842⟩
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