This work revolves around lightwave power transfer networks in which we aim to maximize the number of users served while simultaneously minimizing the transmit power. By formulating the problem as a reinforcement learning (RL) problem, we propose the use of the evolution strategies (ES) method as a novel solution. In this context, ES is a heuristic search method inspired from the biological evolution of nature and it is used to solve complex machine learning problems. Hence, a learning scenario and an ES-based algorithm are devised to solve the RL problem. The results demonstrate that the proposed approach can achieve considerable performance gains compared to the conventional Q-learning method.

中文翻译：

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光波电力传输网络的演进策略

这项工作围绕着光波功率传输网络展开，在该网络中，我们的目标是最大化服务的用户数量，同时最小化传输功率。通过将问题表述为强化学习 (RL) 问题，我们建议使用进化策略 (ES) 方法作为一种新颖的解决方案。在这种情况下，ES 是一种启发式搜索方法，其灵感来自自然界的生物进化，用于解决复杂的机器学习问题。因此，设计了学习场景和基于 ES 的算法来解决 RL 问题。结果表明，与传统的 Q-learning 方法相比，所提出的方法可以获得相当大的性能提升。