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