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v0.5.2
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PaParaZz1 committed Jun 27, 2024
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22 changes: 22 additions & 0 deletions CHANGELOG
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2024.06.27(v0.5.2)
- env: add taxi env (#799) (#807)
- env: add ising model env (#782)
- env: add new Flozen Lake env (#781)
- env: optimize ppo continuous config in MuJoCo (#801)
- env: fix masac smac config multi_agent=True bug (#791)
- env: update/speed up pendulum ppo
- algo: fix gtrxl compatibility bug (#796)
- algo: fix complex obs demo for ppo pipeline (#786)
- algo: add naive PWIL demo
- algo: fix marl nstep td compatibility bug
- feature: add GPU utils (#788)
- feature: add deprecated function decorator (#778)
- style: relax flask requirement (#811)
- style: add new badge (hellogithub) in readme (#805)
- style: update discord link and badge in readme (#795)
- style: fix typo in config.py (#776)
- style: polish rl_utils api docs
- style: add constraint about numpy<2
- style: polish macos platform test version to 12
- style: polish ci python version

2024.02.04(v0.5.1)
- env: add MADDPG pettingzoo example (#774)
- env: polish NGU Atari configs (#767)
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22 changes: 15 additions & 7 deletions README.md
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</div>
<br>

Updated on 2024.02.04 DI-engine-v0.5.1
Updated on 2024.06.27 DI-engine-v0.5.2

## Introduction to DI-engine

Expand All @@ -56,10 +56,13 @@ It provides **python-first** and **asynchronous-native** task and middleware abs
- Multi-agent RL algorithms: such as QMIX, WQMIX, MAPPO, HAPPO, ACE
- Imitation learning algorithms (BC/IRL/GAIL): such as GAIL, SQIL, Guided Cost Learning, Implicit BC
- Offline RL algorithms: BCQ, CQL, TD3BC, Decision Transformer, EDAC, Diffuser, Decision Diffuser, SO2
- Model-based RL algorithms: SVG, STEVE, MBPO, DDPPO, DreamerV3, MuZero
- Model-based RL algorithms: SVG, STEVE, MBPO, DDPPO, DreamerV3
- Exploration algorithms: HER, RND, ICM, NGU
- LLM + RL Algorithms: PPO-max, DPO, MODPO,PromptPG
- LLM + RL Algorithms: PPO-max, DPO, PromptPG
- Other algorithms: such as PER, PLR, PCGrad
- MCTS + RL algorithms: AlphaZero, MuZero, please refer to [LightZero](https://github.com/opendilab/LightZero)
- Generative Model + RL algorithms: Diffusion-QL, QGPO, SRPO, please refer to [GenerativeRL](https://github.com/opendilab/GenerativeRL)


**DI-engine** aims to **standardize different Decision Intelligence environments and applications**, supporting both academic research and prototype applications. Various training pipelines and customized decision AI applications are also supported:

Expand All @@ -72,6 +75,7 @@ It provides **python-first** and **asynchronous-native** task and middleware abs
- [PPOxFamily](https://github.com/opendilab/PPOxFamily): PPO x Family DRL Tutorial Course
- Real world decision AI applications
- [DI-star](https://github.com/opendilab/DI-star): Decision AI in StarCraftII
- [PsyDI](https://github.com/opendilab/PsyDI): Towards a Multi-Modal and Interactive Chatbot for Psychological Assessments
- [DI-drive](https://github.com/opendilab/DI-drive): Auto-driving platform
- [DI-sheep](https://github.com/opendilab/DI-sheep): Decision AI in 3 Tiles Game
- [DI-smartcross](https://github.com/opendilab/DI-smartcross): Decision AI in Traffic Light Control
Expand All @@ -84,16 +88,20 @@ It provides **python-first** and **asynchronous-native** task and middleware abs
- [DOS](https://github.com/opendilab/DOS): [CVPR 2023] ReasonNet: End-to-End Driving with Temporal and Global Reasoning
- [LightZero](https://github.com/opendilab/LightZero): [NeurIPS 2023 Spotlight] A lightweight and efficient MCTS/AlphaZero/MuZero algorithm toolkit
- [SO2](https://github.com/opendilab/SO2): [AAAI 2024] A Perspective of Q-value Estimation on Offline-to-Online Reinforcement Learning
- [LMDrive](https://github.com/opendilab/LMDrive): LMDrive: Closed-Loop End-to-End Driving with Large Language Models
- [LMDrive](https://github.com/opendilab/LMDrive): [CVPR 2024] LMDrive: Closed-Loop End-to-End Driving with Large Language Models
- [SmartRefine](https://github.com/opendilab/SmartRefine): [CVPR 2024] SmartRefine: A Scenario-Adaptive Refinement Framework for Efficient Motion Prediction
- [ReZero](https://github.com/opendilab/LightZero): Boosting MCTS-based Algorithms by Backward-view and Entire-buffer Reanalyze
- [UniZero](https://github.com/opendilab/LightZero): Generalized and Efficient Planning with Scalable Latent World Models
- Docs and Tutorials
- [DI-engine-docs](https://github.com/opendilab/DI-engine-docs): Tutorials, best practice and the API reference.
- [awesome-model-based-RL](https://github.com/opendilab/awesome-model-based-RL): A curated list of awesome Model-Based RL resources
- [awesome-exploration-RL](https://github.com/opendilab/awesome-exploration-rl): A curated list of awesome exploration RL resources
- [awesome-decision-transformer](https://github.com/opendilab/awesome-decision-transformer): A curated list of Decision Transformer resources
- [awesome-RLHF](https://github.com/opendilab/awesome-RLHF): A curated list of reinforcement learning with human feedback resources
- [awesome-multi-modal-reinforcement-learning](https://github.com/opendilab/awesome-multi-modal-reinforcement-learning): A curated list of Multi-Modal Reinforcement Learning resources
- [awesome-AI-based-protein-design](https://github.com/opendilab/awesome-AI-based-protein-design): a collection of research papers for AI-based protein design
- [awesome-diffusion-model-in-rl](https://github.com/opendilab/awesome-diffusion-model-in-rl): A curated list of Diffusion Model in RL resources
- [awesome-ui-agents](https://github.com/opendilab/awesome-ui-agents): A curated list of of awesome UI agents resources, encompassing Web, App, OS, and beyond
- [awesome-AI-based-protein-design](https://github.com/opendilab/awesome-AI-based-protein-design): a collection of research papers for AI-based protein design
- [awesome-end-to-end-autonomous-driving](https://github.com/opendilab/awesome-end-to-end-autonomous-driving): A curated list of awesome End-to-End Autonomous Driving resources
- [awesome-driving-behavior-prediction](https://github.com/opendilab/awesome-driving-behavior-prediction): A collection of research papers for Driving Behavior Prediction

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```latex
@misc{ding,
title={DI-engine: OpenDILab Decision Intelligence Engine},
author={OpenDILab Contributors},
title={DI-engine: A Universal AI System/Engine for Decision Intelligence},
author={Yazhe Niu, Jingxin Xu, Yuan Pu, Yunpeng Nie, Jinouwen Zhang, Shuai Hu, Liangxuan Zhao, Ming Zhang, Yu Liu},
publisher={GitHub},
howpublished={\url{https://github.com/opendilab/DI-engine}},
year={2021},
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2 changes: 1 addition & 1 deletion conda/meta.yaml
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{% set data = load_setup_py_data() %}
package:
name: di-engine
version: v0.5.1
version: v0.5.2

source:
path: ..
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2 changes: 1 addition & 1 deletion ding/__init__.py
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import os

__TITLE__ = 'DI-engine'
__VERSION__ = 'v0.5.1'
__VERSION__ = 'v0.5.2'
__DESCRIPTION__ = 'Decision AI Engine'
__AUTHOR__ = "OpenDILab Contributors"
__AUTHOR_EMAIL__ = "[email protected]"
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