Hashimoto-ML

pymdp

A Python implementation of active inference for Markov Decision Processes

mpot

Implementation of Motion Planning via Optimal Transport (MPOT) in PyTorch, NeurIPS 2023.

condot

Supervised Training of Conditional Monge Maps

purejaxrl

Really Fast End-to-End Jax RL Implementations

tiny-tiny-diffusion

minimal diffusion model for self-study

flashbax

⚡ Flashbax: Accelerated Replay Buffers in JAX

rliable

[NeurIPS'21 Outstanding Paper] Library for reliable evaluation on RL and ML benchmarks, even with only a handful of seeds.

goal_conditioned_rl

Goal-conditioned reinforcement learning like 🔥

skrl

Modular reinforcement learning library (on PyTorch and JAX) with support for NVIDIA Isaac Gym, Omniverse Isaac Gym and Isaac Lab

panda_mujoco_gym

cart_pole

A physical and virtual cartpole

cs287-advanced-robotics

Optimal Control, Reinforcement Learning & Robotics Projects in CS 287: Advanced Robotics (fall 2019)

robohive

A unified framework for robot learning

iLQG-MuJoCo

Iterative LQG for a couple of MuJoCo models

awesome-model-based-RL

A curated list of awesome model based RL resources (continually updated)

jumanji

🕹️ A diverse suite of scalable reinforcement learning environments in JAX

awesome-implicit-neural-models

jaxopt

Hardware accelerated, batchable and differentiable optimizers in JAX.

theseus

A library for differentiable nonlinear optimization

vrkn

Code for "On Uncertainty in Deep State Space Models for Model-Based Reinforcement Learning" (TMLR, 2022)

Gymnasium

An API standard for single-agent reinforcement learning environments, with popular reference environments and related utilities (formerly Gym)

dmcontrol-generalization-benchmark

DMControl Generalization Benchmark

pytorch_mppi

Model Predictive Path Integral (MPPI) with approximate dynamics implemented in pytorch

sts-jax

Structural Time Series in JAX

pykoopman

A package for computing data-driven approximations to the Koopman operator.

aiaiart

Course content and resources for the AIAIART course.

S5

annotated-s4

Implementation of https://srush.github.io/annotated-s4

pysindy

A package for the sparse identification of nonlinear dynamical systems from data

databook_python

IPython notebooks with demo code intended as a companion to the book "Data-Driven Science and Engineering: Machine Learning, Dynamical Systems, and Control" by Steven L. Brunton and J. Nathan Kutz