We present LLM-ABR, the first system that utilizes the generative capabilities of large language models (LLMs) to autonomously design adaptive bitrate (ABR) algorithms tailored for diverse network characteristics. Operating within a reinforcement learning framework, LLM-ABR empowers LLMs to design key components such as states and neural network architectures. We evaluate LLM-ABR across diverse network settings, including broadband, satellite, 4G, and 5G. LLM-ABR consistently outperforms default ABR algorithms.

By "higher-level" they mean higher abstraction level, which is what we're after. So we have our 7 lines of code for a multi-layer neural net. This is magnificent -- 5 lines of code to define our neural net structure (input 2 hidden output regression), 2 lines to train it. Our notebook code is here. Let's go through this in detail, you'll notice that the data and learning intent is identical to our earlier example.

Using the TensorFlow framework directly is a lot of hard work. Its API is extremely verbose and prone to subtle, hard-to-catch bugs. The framework, in general, has a very steep learning curve too. That's probably why many developers today prefer using third-party wrapper frameworks over it, which offer higher-level and more intuitive APIs. The most widely-used ones among them are TFLearn and Keras. Both do get the job done, and are extremely easy to learn.

By "higher-level" they mean higher abstraction level, which is what we're after. So we have our 7 lines of code for a multi-layer neural net. This is magnificent -- 5 lines of code to define our neural net structure (input 2 hidden output regression), 2 lines to train it. Let's go through this in detail, you'll notice that the data and learning intent is identical to our earlier example.

Deep Reinforcement Learning has recently gained a lot of traction in the machine learning community due to the significant amount of progress that has been made in the past few years. Traditionally, reinforcement learning algorithms were constrained to tiny, discretized grid worlds, which seriously inhibited them from gaining credibility as being viable machine learning tools. Here's a classic example from Richard Sutton's book, which I will be referencing a lot. After Deep Q-Networks [4] became a hit, people realized that deep learning methods could be used to solve high-dimensional problems. One of the subsequent challenges that the reinforcement learning community faced was figuring out how to deal with continuous action spaces.

TFlearn is a modular and transparent deep learning library built on top of Tensorflow. It was designed to provide a higher-level API to TensorFlow in order to facilitate and speed-up experimentations, while remaining fully transparent and compatible with it. The high-level API currently supports most of recent deep learning models, such as Convolutions, LSTM, BiRNN, BatchNorm, PReLU, Residual networks, Generative networks... In the future, TFLearn is also intended to stay up-to-date with latest deep learning techniques. Note: This is the first release of TFLearn.

TFlearn is a modular and transparent deep learning library built on top of Tensorflow. It was designed to provide a higher-level API to TensorFlow in order to facilitate and speed-up experimentations, while remaining fully transparent and compatible with it. The high-level API currently supports most of recent deep learning models, such as Convolutions, LSTM, BiRNN, BatchNorm, PReLU, Residual networks, Generative networks... In the future, TFLearn is also intended to stay up-to-date with latest deep learning techniques. Note: This is the first release of TFLearn.