pufferlib

To speed up reinforcement learning training, you can use a high-performance framework that supports vectorized environments and parallel simulation, achieving millions of steps per second. This framework utilizes the PuffeRL algorithm to optimize policy training and environment execution.

Yes, you can seamlessly integrate environments from Gymnasium and PettingZoo for multi-agent reinforcement learning training. The framework directly supports these libraries to facilitate fast parallel training and environment development.

The best way to create custom high-performance environments is by using the PufferEnv API. This allows you to develop tailored environments optimized for speed and scale, facilitating rapid iteration on algorithms and hyperparameters.

Yes, the framework works with challenging environments like Atari and Procgen. It is optimized to train complex reinforcement learning agents on these environments significantly faster than standard libraries.

To train an RL agent using 256 parallel environments, use the framework's quick start configuration to run parallel training on environments like procgen-coinrun. This achieves significant speedups over standard implementations by optimizing environment simulation.