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, Equinox, and Diffrax.

Installation

pip install lerax

Training Example

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