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# NAVIX: minigrid in JAX
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[![arXiv](https://img.shields.io/badge/arXiv-2407.19396-b31b1b.svg?style=flat)](https://arxiv.org/abs/2407.19396)

**[Quickstart](#what-is-navix)** | **[Install](#installation)** | **[Performance](#performance)** | **[Examples](#examples)** | **[Docs](https://epignatelli.com/navix)** | **[The JAX ecosystem](#jax-ecosystem-for-rl)** | **[Contribute](#join-us)** | **[Cite](#cite)**

</div>

## What is NAVIX?
NAVIX is a JAX-powered reimplementation of [MiniGrid](https://github.com/Farama-Foundation/Minigrid). Experiments that took <ins>**1 week**</ins>, now take <ins>**15 minutes**</ins>.    

160 000x speedups compared to MiniGrid and 670 Million steps/s are not just a speed improvements. They produce a whole new paradigm that grants access to experiments that were previously impossible, e.g., those taking years to run.

It changes the game.    
Check out the NAVIX [performance](#performance) more in detail and the [documentation](https://epignatelli.com/navix) for more information.

Key features:
- Performance Boost: NAVIX offers <ins>**over 160 000x**</ins> speed increase compared to the original Minigrid implementation, enabling faster experimentation and scaling. You can see a preliminary performance comparison [here](docs/performance.py), and a full benchmarking at [here](benchmarks/).
- XLA Compilation: Leverage the power of XLA to optimize NAVIX computations for many accelerators. NAVIX can run on CPU, GPU, and TPU.
- Autograd Support: Differentiate through environment transitions, opening up new possibilities such as learned world models.
- Batched hyperparameter tuning: run thousands of experiments in parallel, enabling hyperparameter tuning at scale. Clear your doubts instantly if your algorithm doesn't work because of the hyperparameters choice.
- It allows finally focus on the method research, and not the engineering.

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](https://github.com/epignatelli/navix/discussions/new?category=general) and let's have a chat!


## Installation
#### Install JAX
Follow the official installation guide for your OS and preferred accelerator: https://github.com/google/jax#installation.

#### Install NAVIX
```bash
pip install navix
```

Or, for the latest version from source:
```bash
pip install git+https://github.com/epignatelli/navix
```

## Performance
NAVIX improves MiniGrid both in execution speed *and* throughput, allowing to run more than 2048 PPO agents in parallel almost 10 times faster than *a single* PPO agent in the original MiniGrid.

![speedup_env](https://github.com/user-attachments/assets/b221048c-1b98-43d8-b09b-2a240412dd81)

NAVIX performs 2048 × 16 × 1M/49s = 668 734 693.88 steps per second (∼ 670 Million steps/s) in batch mode,
while the original Minigrid implementation, running a single PPO agent, performs 1M/240 = 4 166.67 steps per second. This
is a speedup of over 160 000×.
![throughput_ppo](https://github.com/user-attachments/assets/eea6e312-55b4-41c3-adb0-4207c5e78fd1)


## Examples
You can view a full set of examples [here](examples/) (more coming), but here are the most common use cases.

### Compiling a collection step
```python
import jax
import navix as nx
import jax.numpy as jnp


def run(seed):
  env = nx.make('MiniGrid-Empty-8x8-v0') # Create the environment
  key = jax.random.PRNGKey(seed)
  timestep = env.reset(key)
  actions = jax.random.randint(key, (N_TIMESTEPS,), 0, env.action_space.n)

  def body_fun(timestep, action):
      timestep = env.step(action)  # Update the environment state
      return timestep, ()

  return jax.lax.scan(body_fun, timestep, actions)[0]

# Compile the entire training run for maximum performance
final_timestep = jax.jit(jax.vmap(run))(jnp.arange(1000))
```

### Compiling a full training run
```python
import jax
import navix as nx
import jax.numpy as jnp
from jax import random

def run_episode(seed, env, policy):
    """Simulates a single episode with a given policy"""
    key = random.PRNGKey(seed)
    timestep = env.reset(key)
    done = False
    total_reward = 0

    while not done:
        action = policy(timestep.observation)
        timestep, reward, done, _ = env.step(action)
        total_reward += reward

    return total_reward

def train_policy(policy, num_episodes):
    """Trains a policy over multiple parallel episodes"""
    envs = jax.vmap(nx.make, in_axes=0)(['MiniGrid-MultiRoom-N2-S4-v0'] * num_episodes)
    seeds = random.split(random.PRNGKey(0), num_episodes)

    # Compile the entire training loop with XLA
    compiled_episode = jax.jit(run_episode)
    compiled_train = jax.jit(jax.vmap(compiled_episode, in_axes=(0, 0, None)))

    for _ in range(num_episodes):
        rewards = compiled_train(seeds, envs, policy)
        # ... Update the policy based on rewards ...

# Hypothetical policy function
def policy(observation):
   # ... your policy logic ...
   return action

# Start the training
train_policy(policy, num_episodes=100)
```

### Backpropagation through the environment
```python
import jax
import navix as nx
import jax.numpy as jnp
from jax import grad
from flax import struct


class Model(struct.PyTreeNode):
  @nn.compact
  def __call__(self, x):
    # ... your NN here

model = Model()
env = nx.environments.Room(16, 16, 8)

def loss(params, timestep):
  action = jnp.asarray(0)
  pred_obs = model.apply(timestep.observation)
  timestep = env.step(timestep, action)
  return jnp.square(timestep.observation - pred_obs).mean()

key = jax.random.PRNGKey(0)
timestep = env.reset(key)
params = model.init(key, timestep.observation)

gradients = grad(loss)(params, timestep)
```

## JAX ecosystem for RL
NAVIX is not alone and part of an ecosystem of JAX-powered modules for RL. Check out the following projects:
- Environments:
  - [Gymnax](https://github.com/RobertTLange/gymnax): a broad range of RL environments
  - [Brax](https://github.com/google/brax): a physics engine for robotics experiments
  - [EnvPool](https://github.com/sail-sg/envpool): a set of various batched environments
  - [Craftax](https://github.com/MichaelTMatthews/Craftax): a JAX reimplementation of the game of [Crafter](https://github.com/danijar/crafter)
  - [Jumanji](https://github.com/instadeepai/jumanji): another set of diverse environments
  - [PGX](https://github.com/sotetsuk/pgx): board games commonly used for RL, such as backgammon, chess, shogi, and go
  - [JAX-MARL](https://github.com/FLAIROx/JaxMARL): multi-agent RL environments in JAX
  - [Xland-Minigrid](https://github.com/corl-team/xland-minigrid/): a set of JAX-reimplemented grid-world environments
  - [Minimax](https://github.com/facebookresearch/minimax):  a JAX library for RL autocurricula with 120x faster baselines
- Agents:
  - [PureJaxRl](https://github.com/luchris429/purejaxrl): proposing fullly-jitten training routines
  - [Rejax](https://github.com/keraJLi/rejax): a suite of diverse agents, among which, DDPG, DQN, PPO, SAC, TD3
  - [Stoix](https://github.com/EdanToledo/Stoix): useful implementations of popular single-agent RL algorithms in JAX
  - [JAX-CORL](https://github.com/nissymori/JAX-CORL): lean single-file implementations of offline RL algorithms with solid performance reports
  - [Dopamine](https://github.com/google/dopamine): a research framework for fast prototyping of reinforcement learning algorithms
  

## Join Us!

NAVIX is actively developed. If you'd like to contribute to this open-source project, we welcome your involvement! Start a discussion or open a pull request.

Please, consider starring the project if you like NAVIX!

## Cite us, please!
If you use NAVIX please cite it as:

```bibtex
@inproceedings{pignatelli2024navix,
 author = {Pignatelli, Eduardo and Liesen, Jarek and Lange, Robert and Lu, Chris and Castro, Pablo Samuel and Toni, Laura},
 booktitle = {Advances in Neural Information Processing Systems},
 doi = {10.52202/085713-4437},
 editor = {D. Belgrave and C. Zhang and H. Lin and R. Pascanu and P. Koniusz and M. Ghassemi and N. Chen},
 publisher = {Curran Associates, Inc.},
 title = {NAVIX: Scaling MiniGrid Environments with JAX},
 url = {https://proceedings.neurips.cc/paper_files/paper/2025/file/c108bda95007522456be2e4b0794b836-Paper-Datasets_and_Benchmarks_Track.pdf},
 volume = {38, Main Conference},
 year = {2025}
}
```
