The Optical RL-Gym: An open-source toolkit for applying reinforcement learning in optical networks
The Optical RL-Gym: An open-source toolkit for applying reinforcement learning in optical networks
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DOI：
10.1109/icton51198.2020.9203239
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发表时间：
2020-07
期刊：
影响因子：
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通讯作者：
Carlos Natalino;P. Monti
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文献类型：
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作者：
Carlos Natalino;P. Monti
强化学习（RL）正在几个领域取得重要突破（例如，自动驾驶汽车、机器人和网络自动化）。它的成功部分归功于工具包的存在（例如，OpenAI Gym）来实现标准的RL任务。一方面，它们允许快速实施和测试新想法。另一方面，这些工具包通过快速、公平的基准测试确保了轻松的可重复性。RL在光网络研究界也越来越受欢迎，在解决多个用例的同时显示出有希望的结果。然而，在许多情况下，基于RL的解决方案的好处仍然不清楚。一个可能的原因是需要陡峭的学习曲线来为每个特定的用例定制基于RL的框架。反过来，这可能会延迟甚至阻止新思想的发展。本文介绍了光网络强化学习健身房（Optical RL-Gym）11本文介绍的工具包可在以下网址获得：https：//github.com/carlosnatalino/optical-rl-gym.，一个开源工具包，可用于将RL应用于与光网络相关的问题。Optical RL-Gym遵循OpenAI Gym建立的原则，OpenAI Gym是RL环境的事实标准。Optical RL-Gym允许与现有的RL代理快速集成，并可以在几个现有的环境上构建，以实现和解决与光网络研究领域相关的更详细的用例。所提出的工具包的能力和好处说明使用光RL健身房解决两个不同的服务提供问题。
Reinforcement Learning (RL) is leading to important breakthroughs in several areas (e.g., self-driving vehicles, robotics, and network automation). Part of its success is due to the existence of toolkits (e.g., OpenAI Gym) to implement standard RL tasks. On the one hand, they allow for the quick implementation and testing of new ideas. On the other, these toolkits ensure easy reproducibility via quick and fair benchmarking. RL is also gaining traction in the optical networks research community, showing promising results while solving several use cases. However, there are many scenarios where the benefits of RL-based solutions remain still unclear. A possible reason for this is the steep learning curve required to tailor RL-based frameworks to each specific use case. This, in turn, might delay or even prevent the development of new ideas. This paper introduces the Optical Network Reinforcement-Learning-Gym (Optical RL-Gym)11The toolkit presented in this paper is available at: https://github.com/carlosnatalino/optical-rl-gym., an open-source toolkit that can be used to apply RL to problems related to optical networks. The Optical RL-Gym follows the principles established by the OpenAI Gym, the de-facto standard for RL environments. Optical RL-Gym allows for the quick integration with existing RL agents, as well as the possibility to build upon several already available environments to implement and solve more elaborated use cases related to the optical networks research area. The capabilities and the benefits of the proposed toolkit are illustrated by using the Optical RL-Gym to solve two different service provisioning problems.