REINFORCEMENT LEARNING

The New Frontier of Telecommunications

SCENARIOREINFORCEMENT LEARNING

Reinforcement Learning (RL) is, alongside supervised and unsupervised learning, one of the three fundamental paradigms of Machine Learning. These are techniques aimed at enabling a computer to learn to perform a task without being explicitly programmed to do so.

Over the years, RL has proven to be an extremely effective approach in numerous fields. One such field is robotics, allowing machines to perform complex operations like movement, object manipulation, or social navigation. Another notable example is board games, where, thanks to a RL algorithm developed by Google in 2006, it became possible to defeat the world champion of the game Go, historically the last of such games (like chess) to remain undefeated by artificial intelligence.

The general operation of an RL algorithm is quite simple and intuitive, having numerous similarities with how humans learn from experience. The main elements that constitute any RL algorithm, regardless of its specific type, are three:


The core of an RL algorithm is, therefore, given by how the agent interacts with the environment based on the feedback given by the reward. The agent explores the environment by taking actions based on decision-making processes and formal criteria known as policy. It tries on one hand to exploit the acquired knowledge to choose actions that lead to optimal results and on the other hand to explore new scenarios by choosing actions randomly to step out of its "comfort zone" and thus be able to adapt to possible changes in the environment and consider solutions it would not have otherwise considered.

Through this process, the agent's learning phase occurs, in which the policy is updated based on the feedback received.


RL IN TELECOMMUNICATIONS

Numerous technologies are emerging and revolutionizing the world of telecommunications. Networks are becoming increasingly complex, and the performance expected from them is ever higher. It is now clear to everyone that AI is an essential tool to meet these changes, and RL plays a predominant role in this scenario.

The enormous complexity of networks makes them easily escape human supervision, creating the need to rely on algorithms that can reach where humans cannot. RL has proven to be a tool capable of achieving performance far above human levels and, moreover, autonomously, hence one of the reasons it fits into this scenario.

Particularly effective and suitable in the context of telecommunications is the approach that involves using Deep Learning in RL algorithms: Deep Reinforcement Learning. In contexts where the environment is particularly complex (like a network), the agent can use a neural network to process the information it receives from the environment to estimate the quantities that allow it to evaluate the correct action among those possible. This drastically increases the performance and effectiveness of these algorithms and overcomes a series of problems that RL algorithms naturally.


USE CASES

RL, thanks to its versatility, finds applications in numerous scenarios, even very different from each other. Some examples of interest in the telco world are:

• Solving "classic" Machine Learning tasks: the problem is restructured as a decision-making task. An example is time series forecasting. The agent learns to predict future values (action) based on past observations (environment) and an evaluation metric, such as prediction accuracy (reward).

• Routing optimization: based on a metric of interest to be optimized (e.g., bandwidth, delay, etc.), the agent, when a communication request occurs between two nodes, chooses the optimal path to optimize the metric in the long term. This means that optimization will also consider possible future communications following a proactive approach.

USE CASE: INTELLIGENT NOC

RL can be used to create a Network Operation Center (NOC) as it is useful in the following aspects:

• Network automation: The agent collects and processes network and telemetry data to perform actions aimed at ensuring the correct and efficient functioning of the network, taking corrective measures when a fault occurs, or acting proactively on the same. Examples are the dynamic configuration of devices, traffic engineering, or fault prevention.

• Network optimization: Based on metrics of interest, the algorithm can perform optimization tasks to increase network performance, such as routing optimization capable of outperforming common protocols like OSPF or EIGRP.

• Action Recommender Engine (ARE): A recommendation system that supports NOC operators by suggesting steps to take when a problem arises, representing an evolution of the widely used codebook.

ADVANTAGES OF RL

  • Generality

    The agent-environment interaction through rewards makes RL a completely general and versatile approach. It is applicable to an infinite variety of scenarios and objectives, without a strong dependence on data.

  • Adaptability

    The use of deep learning in DRL allows algorithms to exploit the generalization capabilities of neural networks, making them deployable in scenarios very different from those encountered during training and robust to possible network changes.

  • Open source

    RL has numerous and valid solutions that provide open-source frameworks for implementing algorithms. This translates into reduced licensing costs, high versatility, and extensive community support.

THE ROLE OF NET REPLY

Technologies such as 5G, IoT, Edge Computing, and Digital Twins lay their implementation foundations, among others, on AI, making it clear how new technologies and new networks can no longer be thought of without this tool. Mastering Artificial Intelligence is and will be essential to ensuring quality and up-to-date solutions.

At Net Reply, the study of these tools is continuously growing, and the drive for innovation is ever-increasing. We are committed to adopting and experimenting with new technologies to offer cutting-edge and evolving solutions. Our methodology embraces innovation to give new life to solutions consolidated over time, allowing us to enhance existing resources while always keeping a constant and attentive eye on the future.


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