pufferlib

You can scale reinforcement learning training for multi-agent environments using a vectorized execution framework with shared memory. PufferLib supports multi-agent setups and integrates with PettingZoo to enable scalable training.

Yes, you can train RL agents with PPO using custom Gymnasium environments. The framework provides a fast PPO-based trainer and allows you to build custom high-performance environments using the PufferEnv API.

PyTorch supports vectorized environment execution for large-scale RL experiments when paired with a framework like PufferLib. This combination leverages shared memory vectorization to accelerate multi-environment execution.

The best way to build high-performance custom environments for RL training is using the PufferEnv API. It provides shared memory vectorization and multi-environment execution to maximize training speed.

Yes, you need Python and PyTorch to run reinforcement learning workflows with PufferLib. The framework requires these dependencies along with the PufferLib package and numpy to execute its vectorized training.

You can integrate Atari and Procgen environments into a scalable RL training pipeline using PufferLib. The framework natively supports these environments alongside NetHack and other Ocean suite environments for fast training.