# google-deepmind/rlax

Repository: https://github.com/google-deepmind/rlax
Canonical: https://ross.abutalabs.com/products/rlax
Homepage: https://rlax.readthedocs.io
Language: Python
License: Apache-2.0
License Family: permissive
Last push: 2026-08-06T15:43:16+00:00

## Health v2 (maintenance only)
Score: 83/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 96, release rhythm 56, longevity 100
- inputs: {"age_days": 2388, "days_push": 27, "days_rel": 82, "gap_med": 199.5, "n_releases_24m": 3}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1439, forks 106 (observed 2026-08-28T04:04:44.016606+00:00)

## What it is
RLax is a JAX-based library of building blocks for implementing reinforcement learning agents, providing mathematical operations like value functions, return distributions, and policy gradients. It is not a complete algorithm framework but composable functions that can be JIT-compiled for CPU, GPU, and TPU.

## Use cases
- implement reinforcement learning agents in JAX
- compute TD losses and Q-learning updates
- implement distributional value functions
- build policy gradient agents for discrete and continuous actions
- learn general value functions in JAX
- run RL experiments on GPU or TPU with jit compilation

## When to choose
- you are building custom RL agents in JAX and need well-tested mathematical building blocks
- you want composable RL operations rather than monolithic agent implementations
- you need hardware-accelerated RL with jax.jit on CPU, GPU, or TPU

## When to avoid
- you want a complete out-of-the-box RL algorithm or training framework
- you work in PyTorch or TensorFlow rather than the JAX ecosystem
- you need environment implementations or full training pipelines

## Facets
- artifact type: library
- maturity: active
- function: machine-learning, reinforcement-learning
- domain: reinforcement-learning, machine-learning, deep-learning
- platform: python
- tags: jax, reinforcement-learning, deepmind, value-functions, policy-gradients, gpu, tpu

## Member repositories
- google-deepmind/rlax (main) score 83

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:04:44.016606+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 2026-08-30T04:36:36.270371+00:00, confidence not recorded.
  - readme: https://github.com/google-deepmind/rlax (fetched 2026-08-28T04:04:44.016606+00:00, sha 8bb7c9d0ef40)
  - registry_pypi: https://pypi.org/pypi/rlax/json (fetched 2026-08-29T11:47:32.516441+00:00, sha 8d9d97581aa1)
- Data as of 2026-08-30T08:39:29.467469+00:00.