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arXiv 1306.2554cs.NIcs.ITcs.LGmath.IT
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无线网络中的关联问题:一种策略梯度强化学习方法

The association problem in wireless networks: a Policy Gradient Reinforcement Learning approach

KTH, Royal Institute of Technology(瑞典皇家理工学院)

Richard Combes, Ilham El Bouloumi, Stephane Senecal, Zwi Altman

摘要:

提出了一种基于策略梯度强化学习(PGRL)的自优化关联算法,该算法具有可扩展性、稳定性和鲁棒性。术语鲁棒性意味着学习阶段的性能下降应该被禁止或限制在预定义的阈值内。该算法是无模型的(与值迭代相反)和鲁棒的(与Q学习相反)。关联问题被建模为马尔可夫决策过程(MDP)。策略空间是参数化的。参数化家庭的政策,然后作为专家知识的PGRL。PGRL收敛于局部最优,在学习过程中,平均成本单调下降。该解决方案的属性使其成为实际实现的良好候选者。此外,鲁棒性属性允许在"始终在线"学习模式中使用PGRL算法。

英文摘要:

The purpose of this paper is to develop a self-optimized association algorithm based on PGRL (Policy Gradient Reinforcement Learning), which is both scalable, stable and robust. The term robust means that performance degradation in the learning phase should be forbidden or limited to predefined thresholds. The algorithm is model-free (as opposed to Value Iteration) and robust (as opposed to Q-Learning). The association problem is modeled as a Markov Decision Process (MDP). The policy space is parameterized. The parameterized family of policies is then used as expert knowledge for the PGRL. The PGRL converges towards a local optimum and the average cost decreases monotonically during the learning process. The properties of the solution make it a good candidate for practical implementation. Furthermore, the robustness property allows to use the PGRL algorithm in an "always-on" learning mode.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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