1 रिपॉजिटरी
Compiled C extensions that offload performance-critical loops like environment stepping and reward computation for near-native speed.
Distinct from C-Extensions: Distinct from general C-Extensions: specifically targets RL simulation acceleration, not general Python-to-C translation.
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PufferLib is a reinforcement learning framework built around high-speed environment simulation and automatic hyperparameter optimization. It is designed to accelerate the entire RL training pipeline by running simulations at near-native speed and enabling the training of tiny models to super-human performance within seconds. The framework achieves its speed through a single-process training loop that eliminates inter-process communication overhead, vectorized batched simulation for parallel environment execution, and compiled C extensions that offload performance-critical computations. It als
Offloads performance-critical RL simulation loops to compiled C extensions for near-native speed.