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Learning Random Access Schemes for Massive …

This paper investigates various multi-agent reinforcement learning (MARL) techniques for designing grant-free random access (RA) schemes suitable for low …

PP-MARL: Efficient Privacy-Preserving Multi-agent …

To overcome these challenges, we propose PP-MARL, an efficient privacy-preserving learning scheme for MARL. PP-MARL leverages homomorphic encryption (HE) and differential privacy (DP) to protect privacy, while introducing split learning to decrease overheads via reducing the volume of shared messages, and then improve efficiency. ...

Multi-Agent Reinforcement Learning (PPO) with …

This tutorial demonstrates how to use PyTorch and torchrl to solve a Multi-Agent Reinforcement Learning (MARL) problem. A code-only version of this tutorial is available …

Massive Access in 5G and Beyond Ultra-Dense Networks: An MARL …

A Non-Orthogonal Random Access (NORA) scheme to enable the coexistence of Human-Type Communications and MTC for 5G and beyond UDN and presents a Multi-Agent Reinforcement Learning (MARL)-based solution to solve this problem. Power-domain Non-Orthogonal Multiple Access (NOMA) and Ultra-Dense …

Multi-Agent Reinforcement Learning (PPO) with TorchRL …

This tutorial demonstrates how to use PyTorch and torchrl to solve a Multi-Agent Reinforcement Learning (MARL) problem.. A code-only version of this tutorial is available in the TorchRL examples, alongside other simple scripts for many MARL sota-implementations (QMIX, MADDPG, IQL). For ease of use, this tutorial will follow the …

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If you are a teacher, you can get early access to the most recent question papers and mark schemes in Centre Services. Log in to Centre Services. Availability of past papers. Most past papers and mark schemes will be available on our website for a period of three years. This is due to copyright restrictions.

Proximal Policy Optimization Family — MARLlib v1.0.0 …

MAPPO is a robust MARL algorithm for diverse cooperative tasks and can outperform SOTA off-policy methods in more challenging scenarios. Formulating the input to the …

Multi-Agent Reinforcement Learning for …

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  • OpenReviewhttps://openreview/pdf?id=qpsl2dR9twy[PDF]

    COMMUNICATION IN MULTI-AGENT REINFORCEMENT L …

    WEBIn this paper, we formalize and propose a new communication scheme for MARL named Intention sharing (IS) in order to go beyond existing observation-sharing schemes for …

  • Massive Access in 5G and Beyond Ultra-Dense Networks: An MARL …

    Power-domain Non-Orthogonal Multiple Access (NOMA) and Ultra-Dense Network (UDN) are promising candidates to cope with the massive access challenge of Machine-Type Communications (MTC). The power level pool is crucial for NOMA to bring performance gains. The existing related literatures rarely consider the power level pool design …

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    Introduction — MARLlib v1.0.0 documentation

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    The overview of MARL for caching and computation …

    Specifically, the task completion latency of NOMA-paired users was minimized in [11] by jointly optimizing the offloading scheme, caching strategy, computational resource, and …

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