When it comes to choosing the best reinforcement learning (RL) framework for large-scale training, several options stand out based on usability, scalability, and community support. Among these, Ray RLLib and TensorFlow Agents are notably powerful. Ray RLLib is particularly designed for large-scale training and supports distributed execution, allowing you to easily scale your experiments across multiple machines. It has built-in support for various RL algorithms, making it versatile for different applications. Additionally, Ray's architecture facilitates managing resources efficiently, which can be crucial when training complex models.
On the other hand, TensorFlow Agents provides a robust set of building blocks for developing RL algorithms in a well-known environment. Its tight integration with TensorFlow means you can leverage various TensorFlow features, including automatic differentiation and distributed training. This framework makes it easier to set up environments and implement complex neural networks. Although it is slightly less focused on out-of-the-box scalability compared to Ray RLLib, its detailed documentation and rich set of tools make it a solid choice for training RL models, especially if you are already familiar with TensorFlow.
Ultimately, the best choice depends on your specific needs, such as the complexity of your RL environment, existing infrastructure, and team expertise. If you require a straightforward setup with substantial scalability from the get-go, Ray RLLib is likely the best fit. However, if you already have experience with TensorFlow and want in-depth customization for your training process, TensorFlow Agents could be more appropriate. Both frameworks have strong communities and are continually updated, which can help you stay aligned with the latest advancements in RL research and practices.