pufferlib

Speed up reinforcement learning training by using high-performance vectorization to optimize parallel environment simulation, achieving 1M-4M steps per second training throughput. This framework is designed specifically for large-scale multi-agent systems and high-throughput tasks.

Yes, PufferLib seamlessly integrates with existing environments from Gymnasium, PettingZoo, and other environment libraries. This allows you to natively set up and train multi-agent systems without rebuilding your simulation environments from scratch.

Train a reinforcement learning agent by running the quick start script on a standard environment like 'procgen-coinrun'. The framework applies high-performance vectorization to parallelize environment simulation and optimize the overall training pipeline.

The best way to run high-throughput parallel training is using a framework that natively supports multi-agent environment setup and vectorized simulation. This approach optimizes parallel processing to reach millions of steps per second during training.

Yes, you need PufferLib installed to execute these training tasks. It is a required dependency that provides the underlying high-performance vectorization and parallel simulation framework needed to run the reinforcement learning workloads.