semvec/pypi-packages huggingface dataset:
lerax 0.0.5
Deep Reinforcement Learning with JAX and Equinox.
Requires Python: >=3.13

# Lerax: Fully JITable reinforcement learning with Jax.

Lerax is a reinforcement learning library built on top of Jax, designed to facilitate the creation, training, and evaluation of RL agents in a fully JITable manner.
It provides modular components for building custom environments, policies, and training algorithms.

Built on top of [Jax](https://docs.jax.dev/en/latest/index.html), [Equinox](https://docs.kidger.site/equinox/), and [Diffrax](https://docs.kidger.site/diffrax/).

## Installation

```bash
pip install lerax
```

## Training Example

```py
from jax import random as jr

from lerax.algorithm import PPO
from lerax.env import CartPole
from lerax.policy import MLPActorCriticPolicy

env = CartPole()
policy = MLPActorCriticPolicy(env=env, key=jr.key(0))
algo = PPO()

policy = algo.learn(env, policy, total_timesteps=2**16, key=jr.key(1))
```

## Documentation

Check out: [lerax.tedpinkerton.ca](https://lerax.tedpinkerton.ca)
httpprint-Sl1ceOfToast 1.0
A minimal package allowing printing to a http server and the console insted of just the console. Great for monitoring long scripts!
Requires Python: >=3.9

# httpprint

This module changes pythons builtin print method to one that hosts a terminal clone on a flask server.
It needs almost no changes to the code, so it can be integrated easily into your projects.

## Usage

Just add

```
from httpprint import print
```

and you're done! When you run the code, a http server is opened on port 5000. Everything you print to the console also gets shown here. Update the page to view new changes.

## Currently supported terminal behaviours

- `\ŧ` pads to the next multiple of **8** spaces
- `\n` for newlines
- Filtered output: `<` and `>` don't break the entire page

## Planned

- Escape sequences for coloring
champions 0.12.0
Add your description here
Requires Python: >=3.13

# Champions 


This project is still in development and has an experimental status. The Documentation is hosted at [champions-ab0723.gitlab.io](https://champions-ab0723.gitlab.io).



If you want to try this package out you can do it with the following commands:

```bash
git clone https://gitlab.com/gwhe/champions.git
cd champions
uv sync 
cd examples/mnist
download the file from  https://www.kaggle.com/datasets/oddrationale/mnist-in-csv
then open files dc_mninst.yaml and change test_files: and train_files: to the dowloaded 
chmod u+x run.sh
./run.sh 
--- trink some coffe (or espresso) --- 
if job is finished then look at the files at ./eval folder
```

of cource you can also install the package with pip (uv add champion). More infos can be found in the docu.