Quickstart | Installation | Examples | Cite
NAVIX is minigrid in JAX, >1000x faster with Autograd and XLA support. You can see a superficial performance comparison here.
The library is in active development, and we are working on adding more environments and features. If you want join the development and contribute, please open a discussion and let's have a chat!
We currently support the OSs supported by JAX. You can find a description here.
You might want to follow the same guide to install jax for your faviourite accelerator (e.g. CPU, GPU, or TPU ).
Then, install the stable version of navix
and its dependencies with:
pip install navix
Or, if you prefer to install the latest version from source:
pip install git+https://github.com/epignatelli/navix
One straightforward use case is to accelerate the computation of the environment with XLA compilation. For example, here we vectorise the environment to run multiple environments in parallel, and compile the full training run.
You can find a partial performance comparison with minigrid in the docs.
import jax
import navix as nx
def run(seed)
env = nx.environments.Room(16, 16, 8)
key = jax.random.PRNGKey(seed)
timestep = env.reset(key)
actions = jax.random.randint(key, (N_TIMESTEPS,), 0, 6)
def body_fun(timestep, action):
timestep = env.step(timestep, jnp.asarray(action))
return timestep, ()
return jax.lax.scan(body_fun, timestep, jnp.asarray(actions, dtype=jnp.int32))[0]
final_timestep = jax.jit(jax.vmap(run))(jax.numpy.arange(1000))
Another use case it to backpropagate through the environment transition function, for example to learn a world model.
TODO(epignatelli): add example.
If you use navix
please consider citing it as:
@misc{pignatelli2023navix,
author = {Pignatelli, Eduardo},
title = {Navix: Accelerated gridworld navigation with JAX},
year = {2023},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {\url{https://github.com/epignatelli/navix}}
}