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PufferLib: Making Reinforcement Learning Libraries and Environments Play Nice

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arxiv 2406.12905 v1 pith:K7JDIDIE submitted 2024-06-11 cs.LG cs.AIcs.MA

Joseph Suarez This is my paper · ORCID
classification cs.LGcs.AIcs.MA
keywords likepufferlibenvironmentenvironmentslearninglibrarieslibrarynice
verification ladder T0 review T1 audit T2 compute T3 formal
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You have an environment, a model, and a reinforcement learning library that are designed to work together but don't. PufferLib makes them play nice. The library provides one-line environment wrappers that eliminate common compatibility problems and fast vectorization to accelerate training. With PufferLib, you can use familiar libraries like CleanRL and SB3 to scale from classic benchmarks like Atari and Procgen to complex simulators like NetHack and Neural MMO. We release pip packages and prebuilt images with dependencies for dozens of environments. All of our code is free and open-source software under the MIT license, complete with baselines, documentation, and support at pufferai.github.io.

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Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Pictura: Perspective-View Self-Play at Scale for Driving

    cs.CV 2026-07 conditional novelty 7.0 of 10

    Perspective-image self-play at 50B agent steps produces a driving policy that approaches privileged-vector performance in-domain and transfers better to re-rendered Waymo layouts.

  2. TerraTransfer: Learning End-to-End Driving Policies Without Expert Demonstrations

    cs.CV 2026-06 unverdicted novelty 6.0 of 10

    TerraTransfer decouples self-play policy pretraining from vision alignment via KL divergence and low-rank loss to produce end-to-end driving policies without expert demonstrations, matching prior methods on closed-loo...

  3. The challenge of hidden gifts in multi-agent reinforcement learning

    cs.LG 2025-05 unverdicted novelty 6.0 of 10

    Standard MARL algorithms collapse on the Manitokan hidden-gift task, while actor-critic agents with action history and a self-correction term reliably learn to leave the key.

  4. Combining Pre-Trained Models for Enhanced Feature Representation in Reinforcement Learning

    cs.LG 2025-07 conditional novelty 5.0 of 10

    A lightweight attention module that weights embeddings from multiple pre-trained models achieves comparable Atari RL performance to end-to-end training, with improved robustness to visual changes.

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