pufferlib

You can run fast reinforcement learning experiments using PufferLib's high-performance, vectorized framework with ready-to-run templates. It natively supports multi-agent environments across Gymnasium, PettingZoo, and Procgen for scalable training.

Vectorizing Gymnasium environments for PyTorch reinforcement learning is best handled by a high-performance vectorized framework like PufferLib, which processes native vectorized environments to achieve millions of steps per second.

Yes, PufferLib supports multi-agent training in PettingZoo environments. It provides native multi-agent support and seamless integrations, allowing you to train agents in complex multi-agent settings using provided templates.

Yes, you need PyTorch to use PufferLib for reinforcement learning. The framework requires PyTorch, Gymnasium, PettingZoo, and the PufferLib toolkit to automate scalable training experiments and support LSTM integration.

You can bootstrap reinforcement learning experiments by installing PufferLib and running a sample training pipeline. The framework provides ready-to-use training, environment, and reference templates to quickly start your scalable RL research.

Yes, you can integrate LSTM models into vectorized reinforcement learning environments. PufferLib supports LSTM integration and vectorized data flow to train agents efficiently in both single- and multi-environment setups.