📦 Quick boilerplate to get you started with TailwindCSS

Implementation of Interpretable and Editable Programmatic Tree Policies for Reinforcement Learning (Kohler, Delfosse, et. al. 2024).

Sequential testing for efficient and reliable comparison of stochastic algorithms.

Formulating Model-based RL Dynamics as a continuous rather then one step prediction

The tiniest of Gaussian Process libraries

Gaussian processes in JAX.

MBRL library in JAX

API to run VirtualHome, a Multi-Agent Household Simulator

Open source collection of Reinforcement Learning Environments.

Accompanying code for "Learning and Planning in Average-Reward Markov Decision Processes"

🏛️A research-friendly codebase for fast experimentation of single-agent reinforcement learning in JAX • End-to-End JAX RL

A simulation of the robot navigation problem in Gymnasium.

Simplifying reinforcement learning for complex game environments

Mastering Diverse Domains through World Models

Deep reinforcement learning library built on top of Neural Network Libraries

🕹️ A diverse suite of scalable reinforcement learning environments in JAX

Accelerated machine learning with dependent types

⚡ Flashbax: Accelerated Replay Buffers in JAX

tensorboard for pytorch (and chainer, mxnet, numpy, ...)

RL Environments in JAX 🌍

Add a tqdm progress bar to your JAX scans and loops.

Really Fast End-to-End Jax RL Implementations