DIRAL:用于5G-V2V通信的具有多智能体深度强化学习的分布式资源分配-源码

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拨盘 分布式资源分配与Multi-Agent的深强化学习的5G-V2V通信 。 此存储库包含玩具示例的源代码,我们在本文中使用了该示例来测试算法的性能。 抽象的 我们考虑在没有基站的情况下在车对车(V2V)通信中的分布式资源选择问题。 每辆车从共享资源池中自主选择传输资源,以传播合作意识消息(CAM)。 这是每个车辆必须选择唯一资源的共识问题。 当由于移动性而彼此相邻的车辆数量在动态变化时,这个问题变得更具挑战性。 在拥挤的情况下,为每辆车分配唯一资源变得不可行,并且必须开发拥挤的资源分配策略。 5G中的标准化方法,即半永久性调度(SPS)受车辆空间分布造成的影响。 在我们的方法中,我们将其转化为优势。 我们提出了一种使用多主体强化学习(DIRAL)的新颖的DIstributed资源分配机制,该机制建立在唯一的状态表示之上。 一个具有挑战性的问题是应对并发学习代理引入的非平稳性,这会导致多

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