jymkit 0.0.17

A lightweight utility library for reinforcement learning projects in JAX and Equinox.

JymKit: A Lightweight Utility Library for JAX-based RL Projects

JymKit lets you

1. 🕹️ Import your favourite environments from various libraries with a single API and automatically wrap them to a common standard.
2. 🚀 Bootstrap new JAX RL projects with a single CLI command and get started instantly with a complete codebase.
3. 🤖 JymKit comes equiped with standard general RL implementations based on a near-single-file philosophy. You can either import these as off-the-shelf algorithms or copy over the code and tweak them for your problem. These algorithms follow the ideas of PureJaxRL for extremely fast end-to-end RL training in JAX.

📖 More details over at the Documentation

🚀 Getting started

JymKit lets you bootstrap your new reinforcement learning projects directly from the command line. As such, for new projects, the easiest way to get started is via uv:

uvx jymkit <projectname>
uv run example_train.py

# ... or via pipx
pipx run jymkit <projectname>
# activate a virtual environment in your preferred way, e.g. conda
python example_train.py

This will set up a Python project folder structure with (optionally) an environment template and (optionally) algorithm code for you to tailor to your problem.

For existing projects, you can simply install JymKit via pip and import the required functionality.

pip install jymkit

import jax
import jymkit as jym
from jymkit.algorithms import PPO

env = jym.make("CartPole")
env = jymkit.LogWrapper(env)
rng = jax.random.PRNGKey(0)
agent = PPO(total_timesteps=5e5, learning_rate=2.5e-3)
agent = agent.train(rng, env)

🏠 Environments

JymKit is not aimed at delivering a full environment suite. However, it does come equipped with a jym.make(...) command to import environments from existing suites (provided that these are installed) and wrap them appropriately to the JymKit API standard. For example, using environments from Gymnax:

import jymkit as jym
from jymkit.algorithms import PPO
import jax

env = jym.make("Breakout-MinAtar")
env = jym.FlattenObservationWrapper(env)
env = jym.LogWrapper(env)

agent = PPO(**some_good_hyperparameters)
agent = agent.train(jax.random.PRNGKey(0), env)

# > Using an environment from Gymnax via gymnax.make(Breakout-MinAtar).
# > Wrapping Gymnax environment with GymnaxWrapper
# >  Disable this behavior by passing wrapper=False
# > Wrapping environment in VecEnvWrapper
# > ... training results

For convenience, JymKit does include the 5 classic-control environments.

Currently, importing from external libraries is possible for Gymnax and Brax. More are coming up!

Environment API

The JymKit API stays close to the somewhat established Gymnax API for the reset() and step() functions, but allows for truncated episodes in a manner closer to Gymnasium.

env = jym.make(...)

obs, env_state = env.reset(key) # <-- Mirroring Gymnax

# env.step(): Gymnasium Timestep tuple with state information
(obs, reward, terminated, truncated, info), env_state = env.step(key, state, action)

🤖 Algorithms

Algorithms in jymkit.algorithms are built following a near-single-file implementation philosophy in mind. In contrast to implementations in CleanRL or PureJaxRL, JymKit algorithms are built in Equinox and follow a class-based design with a familiar Stable-Baselines API.

Each algorithm supports both discrete- and continuous action/observation space -- adjusting based on the provided environment observation_space and action_space. Additionally, the implementations support multi-agent environments out of the box.

from jymkit.algorithms import PPO
import jax

env = ...
agent = PPO(**some_good_hyperparameters)
agent = agent.train(jax.random.PRNGKey(0), env)

Currently, only a PPO implementation is implemented. More will be included in the near future. However, the current goal is not to include as many algorithms as possible.