pufferlib

PufferLib is a high-performance reinforcement learning framework designed to address the challenges associated with slow training times and the complexity of multi-agent systems. By leveraging optimized vectorization and efficient implementation of the Proximal Policy Optimization (PPO) algorithm, PufferLib achieves training speeds of millions of steps per second. This capability makes it an ideal solution for researchers and developers who need rapid experimentation and deployment of reinforcement learning agents in diverse environments, including games and simulations.