Accelerate reinforcement learning training with parallel, zero-copy vectorized environments.
npx skills add https://github.com/Clay-HHK/claude-config --skill pufferlib-clay-hhk
Please help me install this Agent Skill. Skill: pufferlib Source: https://github.com/Clay-HHK/claude-config/tree/main/skills/pufferlib Command: npx skills add https://github.com/Clay-HHK/claude-config --skill pufferlib-clay-hhk
PufferLib eliminates slow, single-threaded reinforcement learning workflows by providing highly optimized parallel environment simulation and training so researchers and engineers can iterate faster and scale experiments without rewriting environment code.
Use pufferlib to train a PPO agent on procgen-coinrun with 256 vectorized environments on GPU.
High-intent search queries and answers about installing and using this skill.
Accelerate reinforcement learning training by using optimized PPO+LSTM trainers and zero-copy shared-memory vectorized environments, enabling millions of steps per second on GPU-backed training.
Yes, you can use PyTorch for multi-agent PPO training with PettingZoo environments, as native multi-agent support and seamless integration with PettingZoo are provided.
Vectorize Gymnasium environments for high-throughput RL by using zero-copy shared-memory buffers, busy-wait worker flags, and hierarchical vectorization across serial, multiprocessing, and async modes.
Distributed multi-GPU training works with PPO and LSTM policies through integrated torchrun support, allowing you to scale experiments without rewriting environment code.
You do not need to build from scratch, as templates and reference guides for environment development, policy architectures, and custom PufferEnv implementations are provided.
Single-threaded reinforcement learning is slow due to bottlenecks, which vectorization fixes by implementing parallel environment simulation with shared-memory zero-copy buffers and busy-wait workers.
SciMate-AITrain reinforcement learning agents with vectorized environments across Gymnasium, PettingZoo, and Ocean.
pacphiTrains and deploys neural networks in distributed E2B sandbox environments.
yf8578Train PPO agents in vectorized environments with pufferlib.
ovachieverTrain PPO/LSTM agents with vectorized environments and shared-memory buffers.
