论文标题
从固定数据中学习电源控制
Learning Power Control from a Fixed Batch of Data
论文作者
论文摘要
我们讨论如何利用从受监视的环境中收集的电源控制数据，以在未开发的环境中执行电源控制。我们采用离线深入的强化学习，在该学习中，代理商会学习仅通过使用数据来产生传输权力的政策。实验表明，尽管受监视和未开发的环境之间存在差异，但代理还是成功地学习了功率控制，即使受监视和未经探索的环境中的目标函数不同。大约三分之一的收集数据足以具有高质量，其余的可以来自任何亚最佳算法。
We address how to exploit power control data, gathered from a monitored environment, for performing power control in an unexplored environment. We adopt offline deep reinforcement learning, whereby the agent learns the policy to produce the transmission powers solely by using the data. Experiments demonstrate that despite discrepancies between the monitored and unexplored environments, the agent successfully learns the power control very quickly, even if the objective functions in the monitored and unexplored environments are dissimilar. About one third of the collected data is sufficient to be of high-quality and the rest can be from any sub-optimal algorithm.
