# luchris429/purejaxrl

Really Fast End-to-End Jax RL Implementations

Repository: https://github.com/luchris429/purejaxrl
Canonical: https://ross.abutalabs.com/products/purejaxrl
Language: Python
License: Apache-2.0
License Family: permissive
Topics: deep-reinforcement-learning, jax, reinforcement-learning, reinforcement-learning-algorithms, ppo
Last push: 2024-09-09T19:05:43+00:00

## Health v2 (maintenance only)
Score: 30/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 0, release rhythm 35, longevity 91
- inputs: {"age_days": 1285, "days_push": 723, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1099, forks 87 (observed 2026-08-28T04:03:35.076053+00:00)

## What it is
PureJaxRL provides end-to-end reinforcement learning training pipelines implemented entirely in JAX, including environments, enabling massive speedups via JIT compilation and vectorization. It offers CleanRL-style single-file implementations like PPO that can run thousands of parallel agent seeds on a single GPU.

## Use cases
- train PPO agents on GPU with JAX
- run thousands of RL seeds in parallel for hyperparameter tuning
- speed up reinforcement learning training 1000x over PyTorch
- do meta-evolution and meta-RL research
- learn single-file RL algorithm implementations
- benchmark RL algorithms on MinAtar and Cartpole

## When to choose
- you need extremely fast RL training with many parallel seeds on GPUs
- you want to jit/vmap/pmap entire RL training pipelines including environments
- you're doing RL research like meta-evolution or rapid hyperparameter sweeps
- you prefer CleanRL-style readable single-file implementations

## When to avoid
- you need a modular, importable RL library for production applications
- you require CPU-only training or non-JAX ecosystems like PyTorch
- you need a wide algorithm coverage beyond the provided PPO variants
- you want stable APIs and long-term maintenance guarantees

## Facets
- artifact type: library
- maturity: active
- function: reinforcement-learning, machine-learning, gpu-computing, benchmarking
- domain: reinforcement-learning, deep-learning, machine-learning
- platform: python, cloud
- tags: jax, ppo, single-file-implementations, meta-reinforcement-learning, jit-compilation, research-code, research, gpu, linux

## Member repositories
- luchris429/purejaxrl (main) score 30

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:03:35.076053+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T06:46:04.114067+00:00, confidence not recorded.
  - readme: https://github.com/luchris429/purejaxrl (fetched 2026-08-28T04:03:35.076053+00:00, sha 7463f5d14211)
- Data as of 2026-08-30T08:39:29.467469+00:00.