OpenDILab Decision AI Engine. The Most Comprehensive Reinforcement Learning Framework B.P.

An offline deep reinforcement learning library

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

Summary of key papers and blogs about diffusion models to learn about the topic. Detailed list of all published diffusion robotics papers.

Related papers for reinforcement learning, including classic papers and latest papers in top conferences

A standard format for offline reinforcement learning datasets, with popular reference datasets and related utilities

A Japanese (Riichi) Mahjong AI Framework

DI-engine docs (Chinese and English)

🤖 Elegant implementations of offline safe RL algorithms in PyTorch

Official code from the paper "Offline RL for Natural Language Generation with Implicit Language Q Learning"

Python library for solving reinforcement learning (RL) problems using generative models (e.g. Diffusion Models).

Clean single-file implementation of offline RL algorithms in JAX

SCOPE-RL: A python library for offline reinforcement learning, off-policy evaluation, and selection

A large-scale multi-modal pre-trained model

ExORL: Exploratory Data for Offline Reinforcement Learning

🔥 Datasets and env wrappers for offline safe reinforcement learning

official implementation for our paper Cal-QL: Calibrated Offline RL Pre-Training for Efficient Online Fine-Tuning (NeurIPS 2023)

An out-of-the-box GUI tool for offline deep reinforcement learning

Reinforcement Learning Short Course

Extreme Q-Learning: Max Entropy RL without Entropy

Benchmarked implementations of Offline RL Algorithms.

Learning representations for RL in Healthcare under a POMDP assumption

Conservative Q learning in Jax

PyTorch implementation of the implicit Q-learning algorithm (IQL)

code for paper Query-Dependent Prompt Evaluation and Optimization with Offline Inverse Reinforcement Learning

Model-based Offline Policy Optimization re-implement all by pytorch

D2C(Data-driven Control Library) is a library for data-driven control based on reinforcement learning.

Codes for "Efficient Offline Policy Optimization with a Learned Model", ICLR2023

The Medkit-Learn(ing) Environment: Medical Decision Modelling through Simulation (NeurIPS 2021) by Alex J. Chan, Ioana Bica, Alihan Huyuk, Daniel Jarrett, and Mihaela van der Schaar.

Code for the paper "Showing Your Offline Reinforcement Learning Work: Online Evaluation Budget Matters", ICML 2022