The fundamental package for scientific computing with Python.

JAX reimplementation of the DeepMind paper "Genie: Generative Interactive Environments"

A collection of MARL benchmarks based on TorchRL

Accelerated minigrid environments with JAX

Deep memory and sequence models in JAX

VMAS is a vectorized differentiable simulator designed for efficient Multi-Agent Reinforcement Learning benchmarking. It is comprised of a vectorized 2D physics engine written in PyTorch and a set …

A tool for aggregating and plotting MARL experiment data.

♟️ Vectorized RL game environments in JAX

Cardio aims to make deep reinforcement learning algorithm implementations easy to do, readable and framework agnostic by providing a collection of modular environment interaction loops for the rese…

This is a repo where I implimented a neural network from scratch using only numpy and impliment the backprop algorithm manually

POPGym Library in JAX

A neural network library written in jax

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

Extensions to Yuan et al. QAit task.

Evolution Strategies in JAX 🦎

🪐 The Sebulba architecture to scale reinforcement learning on Cloud TPUs in JAX

⚡ Flashbax: Accelerated Replay Buffers in JAX

Datasets with baselines for offline multi-agent reinforcement learning.

🕹️ A diverse suite of scalable reinforcement learning environments in JAX

MRP Frameworks graph visualisation and analysis tool

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

Monte Carlo tree search in JAX

🦁 A research-friendly codebase for fast experimentation of multi-agent reinforcement learning in JAX

Implementations of basic RL algorithms with minimal lines of codes! (pytorch based)

Question Answering with Interactive Text (QAit), code for EMNLP 2019 paper "Interactive Language Learning by Question Answering"