pufferlib

Vectorized reinforcement learning training with PPO is automated by configuring multi-environment parallelism and instantiating a training loop with policy architectures and logging hooks. Shared memory observations accelerate the PPO optimization process.

Custom Gymnasium environments can be used for multi-agent RL training by wrapping them as PufferEnv templates. Integration with PettingZoo is also supported, allowing diverse single- and multi-agent task setups within the same framework.

Environment vectorization consolidates environment simulation and policy optimization by running multiple environments in parallel using shared memory. This performance-first design drastically accelerates experimentation and production-grade reinforcement learning workflows.

Both single- and multi-agent workflows are supported through PufferLib's end-to-end PPO training pipelines. It interfaces with Gymnasium for single-agent tasks and PettingZoo for multi-agent parallelism, ensuring scalable RL experiments.

Using PyTorch for high-performance RL training requires managing dependencies like NumPy and shared memory observations to prevent bottlenecks. While PufferLib optimizes policy architectures, simulation scaling is ultimately bounded by available CPU and memory allocation.