Sample Factory: Egocentric 3D Control from Pixels at 100000 FPS with Asynchronous Reinforcement Learning

Sample Factory is a research project that explores the use of asynchronous reinforcement learning to train an agent to control a 3D environment from raw pixels. The focus of the project is on egocentric vision, which means that the agent is equipped with a first-person camera and must learn to navigate and interact with the environment based solely on visual input.

One of the key challenges of training an agent to control a 3D environment is the sheer amount of data required. In order to achieve a high level of proficiency, the agent needs to be exposed to a large number of samples, ideally at a high frame rate. Sample Factory addresses this challenge by using a distributed architecture that allows for the collection of up to 100000 samples per second.

The asynchronous reinforcement learning approach used by Sample Factory is based on the A3C algorithm, which stands for Asynchronous Advantage Actor-Critic. This algorithm is designed to be highly scalable and efficient, making it well-suited for distributed training.

Overall, Sample Factory represents an exciting step forward in the development of AI agents that are capable of interacting with complex 3D environments using only visual input. By leveraging the power of asynchronous reinforcement learning and distributed computing, the project demonstrates the potential of AI to tackle some of the most challenging problems in robotics and automation.