标题： 无线无线电资源管理的平均奖励强化学习 显示英文标题

标题： Average Reward Reinforcement Learning for Wireless Radio Resource Management

摘要： 在本文中，我们解决了一个在将强化学习（RL）应用于无线通信中的无线资源管理（RRM）时至关重要的但常被忽视的问题：折扣奖励RL公式与无线网络优化的无折扣目标之间的不匹配。 据我们所知，我们是第一个系统地研究这一差异的，从问题公式化的讨论开始，随后通过仿真量化了差距的程度。 为了弥合这一差距，我们引入了平均奖励RL的使用，这种方法更符合RRM的长期目标。 我们提出了一种新方法，称为平均奖励离策略软演员评论家（ARO SAC），这是在平均奖励框架下对众所周知的软演员评论家算法的适应。 这种方法在我们的仿真结果中显示出显著的性能提升，系统性能比传统的折扣奖励RL方法提高了15%，这突显了平均奖励RL在提高无线网络优化的效率和效果方面的潜力。 显示英文摘要

摘要： In this paper, we address a crucial but often overlooked issue in applying reinforcement learning (RL) to radio resource management (RRM) in wireless communications: the mismatch between the discounted reward RL formulation and the undiscounted goal of wireless network optimization. To the best of our knowledge, we are the first to systematically investigate this discrepancy, starting with a discussion of the problem formulation followed by simulations that quantify the extent of the gap. To bridge this gap, we introduce the use of average reward RL, a method that aligns more closely with the long-term objectives of RRM. We propose a new method called the Average Reward Off policy Soft Actor Critic (ARO SAC) is an adaptation of the well known Soft Actor Critic algorithm in the average reward framework. This new method achieves significant performance improvement our simulation results demonstrate a 15% gain in the system performance over the traditional discounted reward RL approach, underscoring the potential of average reward RL in enhancing the efficiency and effectiveness of wireless network optimization.