ShenQianli

google-research

Google Research

stable-baselines3

PyTorch version of Stable Baselines, reliable implementations of reinforcement learning algorithms.

arxiv-latex-cleaner

arXiv LaTeX Cleaner: Easily clean the LaTeX code of your paper to submit to arXiv

ElegantRL

Massively Parallel Deep Reinforcement Learning. 🔥

deepxde

A library for scientific machine learning and physics-informed learning

CommandlineConfig

A library for users to write (experiment in research) configurations in Python Dict or JSON format, read and write parameter value via dot . in code, while can read parameters from the command line to modify values. 一个供用户以Python Dict或JSON格式编写（科研中实验）配置的库，在代码中用点.读写属性，同时可以从命令行中读取参数配置并修改参数值。

Minigrid

Simple and easily configurable grid world environments for reinforcement learning

d3rlpy

An offline deep reinforcement learning library

D4RL

A collection of reference environments for offline reinforcement learning

awesome-offline-rl

An index of algorithms for offline reinforcement learning (offline-rl)

PINNpapers

Must-read Papers on Physics-Informed Neural Networks.

torchbeast

A PyTorch Platform for Distributed RL

batch_rl

Offline Reinforcement Learning (aka Batch Reinforcement Learning) on Atari 2600 games

InvariantRiskMinimization

PyTorch code to run synthetic experiments.

CQL

Code for conservative Q-learning

mbbl

Awesome-GFlowNets

A curated list of resources about generative flow networks (GFlowNets).

Meta-Learning-Papers

A classified list of meta learning papers based on realm.

simple_rl

A simple framework for experimenting with Reinforcement Learning in Python.

cule

CuLE: A CUDA port of the Atari Learning Environment (ALE)

awr

Implementation of advantage-weighted regression.

BEAR

Code for Stabilizing Off-Policy RL via Bootstrapping Error Reduction

neuraluq

sunrise

SUNRISE: A Simple Unified Framework for Ensemble Learning in Deep Reinforcement Learning

meta-q-learning

Code for the paper "Meta-Q-Learning"( ICLR 2020)

EB_GFN

Code for our paper "Generative Flow Networks for Discrete Probabilistic Modeling"

DLDR

[TPAMI 2023] Low Dimensional Landscape Hypothesis is True: DNNs can be Trained in Tiny Subspaces

arxiv.black

Enjoy dark mode on Arxiv papers

GFNDropout

Human-Machine-MDP