pufferlib

Vectorized environments scale reinforcement learning by parallelizing environment execution to achieve millions of steps per second. PufferLib automates this high-throughput training using PPO-style pipelines across multiple ecosystem frameworks.

PufferLib supports multi-agent reinforcement learning workflows with PettingZoo. It integrates across PettingZoo, Gymnasium, Procgen, NetHack, and Ocean family environments using a unified PufferEnv-based API.

You can create a custom Gymnasium environment using PufferLib's provided templates and examples, such as env_template.py. These templates accelerate custom environment development and experimentation for isolated, reproducible experiments.

PufferLib requires both numpy and pufferlib as dependencies to run reinforcement learning workflows. These dependencies support the underlying vectorization and isolated workspace management for reproducible experiments.

Reproducible reinforcement learning experiments are managed through isolated workspace environments. PufferLib provides this workspace management alongside high-performance vectorized environments to ensure consistent experimental results.

Vectorized environments are needed to meet high-throughput integration and training workflow requirements. They enable PufferLib to execute PPO-style pipelines at millions of steps per second for scalable reinforcement learning.