offline-rl
There are 52 repositories under offline-rl topic.
opendilab/DI-engine
OpenDILab Decision AI Engine. The Most Comprehensive Reinforcement Learning Framework B.P.
takuseno/d3rlpy
An offline deep reinforcement learning library
hanjuku-kaso/awesome-offline-rl
An index of algorithms for offline reinforcement learning (offline-rl)
mbreuss/diffusion-literature-for-robotics
Summary of key papers and blogs about diffusion models to learn about the topic. Detailed list of all published diffusion robotics papers.
yingchengyang/Reinforcement-Learning-Papers
Related papers for reinforcement learning, including classic papers and latest papers in top conferences
Farama-Foundation/Minari
A standard format for offline reinforcement learning datasets, with popular reference datasets and related utilities
Cryolite/kanachan
A Japanese (Riichi) Mahjong AI Framework
opendilab/DI-engine-docs
DI-engine docs (Chinese and English)
Sea-Snell/Implicit-Language-Q-Learning
Official code from the paper "Offline RL for Natural Language Generation with Implicit Language Q Learning"
liuzuxin/OSRL
🤖 Elegant implementations of offline safe RL algorithms in PyTorch
nissymori/JAX-CORL
Clean single-file implementation of offline RL algorithms in JAX
Shanghai-Digital-Brain-Laboratory/BDM-DB1
A large-scale multi-modal pre-trained model
hakuhodo-technologies/scope-rl
SCOPE-RL: A python library for offline reinforcement learning, off-policy evaluation, and selection
opendilab/GenerativeRL
Python library for solving reinforcement learning (RL) problems using generative models (e.g. Diffusion Models).
denisyarats/exorl
ExORL: Exploratory Data for Offline Reinforcement Learning
takuseno/minerva
An out-of-the-box GUI tool for offline deep reinforcement learning
liuzuxin/DSRL
🔥 Datasets and env wrappers for offline safe reinforcement learning
nakamotoo/Cal-QL
official implementation for our paper Cal-QL: Calibrated Offline RL Pre-Training for Efficient Online Fine-Tuning
Div99/XQL
Extreme Q-Learning: Max Entropy RL without Entropy
LAMDA-RL/OfflineRL-Lib
Benchmarked implementations of Offline RL Algorithms.
callmespring/RL-short-course
Reinforcement Learning Short Course
MLforHealth/rl_representations
Learning representations for RL in Healthcare under a POMDP assumption
young-geng/JaxCQL
Conservative Q learning in Jax
BY571/Implicit-Q-Learning
PyTorch implementation of the implicit Q-learning algorithm (IQL)
holarissun/Prompt-OIRL
code for paper Query-Dependent Prompt Evaluation and Optimization with Offline Inverse Reinforcement Learning
junming-yang/mopo
Model-based Offline Policy Optimization re-implement all by pytorch
XanderJC/medkit-learn
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.
tinkoff-ai/eop
Code for the paper "Showing Your Offline Reinforcement Learning Work: Online Evaluation Budget Matters", ICML 2022
sail-sg/rosmo
Codes for "Efficient Offline Policy Optimization with a Learned Model", ICLR2023
AIR-DI/D2C
D2C(Data-driven Control Library) is a library for data-driven control based on reinforcement learning.
hari-sikchi/offline_rl
Pytorch implementation of state-of-the-art offline reinforcement learning algorithms.
xionghuichen/MAPLE
The Official Code for Offline Model-based Adaptable Policy Learning (NeurIPS'21 & TPAMI)
christopher-beckham/coms-are-energy-models
Official code for paper: Conservative objective models are a special kind of contrastive divergence-based energy model
YiqinYang/VEM
Codes accompanying the paper "Offline Reinforcement Learning with Value-Based Episodic Memory" (ICLR 2022 https://arxiv.org/abs/2110.09796)
samholt/NeuralLaplaceControl
Neural Laplace Control for Continuous-time Delayed Systems - an offline RL method combining Neural Laplace dynamics model and MPC planner to achieve near-expert policy performance in environments with irregular time intervals and an unknown constant delay.
qiwang067/CoWorld
[NeurIPS 2024] PyTorch code for the paper "Making Offline RL Online: Collaborative World Models for Offline Visual Reinforcement Learning"