Updated on 2024.12.23 DI-engine-v0.5.3
Introduction to DI-engine
Documentation | 中文文档 | Tutorials | Feature | Task & Middleware | TreeTensor | Roadmap
DI-engine is a generalized decision intelligence engine for PyTorch and JAX.
It provides python-first and asynchronous-native task and middleware abstractions, and modularly integrates several of the most important decision-making concepts: Env, Policy and Model. Based on the above mechanisms, DI-engine supports various deep reinforcement learning algorithms with superior performance, high efficiency, well-organized documentation and unittest:
- Most basic DRL algorithms: such as DQN, Rainbow, PPO, TD3, SAC, R2D2, IMPALA
- 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
- Exploration algorithms: HER, RND, ICM, NGU
- LLM + RL Algorithms: PPO-max, DPO, PromptPG, PromptAWR
- Other algorithms: such as PER, PLR, PCGrad
- MCTS + RL algorithms: AlphaZero, MuZero, please refer to LightZero
- Generative Model + RL algorithms: Diffusion-QL, QGPO, SRPO, please refer to 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:
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On the low-level end, DI-engine comes with a set of highly re-usable modules, including RL optimization functions, PyTorch utilities and auxiliary tools.
BTW, DI-engine also has some special system optimization and design for efficient and robust large-scale RL training:
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Have fun with exploration and exploitation.
Outline
Installation
You can simply install DI-engine from PyPI with the following command:
pip install DI-engine
For more information about installation, you can refer to installation.
And our dockerhub repo can be found here，we prepare base image and env image with common RL environments.
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- base: opendilab/ding:nightly
- rpc: opendilab/ding:nightly-rpc
- atari: opendilab/ding:nightly-atari
- mujoco: opendilab/ding:nightly-mujoco
- dmc: opendilab/ding:nightly-dmc2gym
- metaworld: opendilab/ding:nightly-metaworld
- smac: opendilab/ding:nightly-smac
- grf: opendilab/ding:nightly-grf
- cityflow: opendilab/ding:nightly-cityflow
- evogym: opendilab/ding:nightly-evogym
- d4rl: opendilab/ding:nightly-d4rl
The detailed documentation are hosted on doc | 中文文档.
Quick Start
3 Minutes Kickoff
3 Minutes Kickoff (colab)
DI-engine Huggingface Kickoff (colab)
How to migrate a new RL Env | 如何迁移一个新的强化学习环境
How to customize the neural network model | 如何定制策略使用的神经网络模型
测试/部署 强化学习策略 的样例
新老 pipeline 的异同对比
Feature
Algorithm Versatility
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discrete means discrete action space, which is only label in normal DRL algorithms (1-23)
means continuous action space, which is only label in normal DRL algorithms (1-23)
means hybrid (discrete + continuous) action space (1-23)
Distributed Reinforcement Learning｜分布式强化学习
Multi-Agent Reinforcement Learning｜多智能体强化学习
Exploration Mechanisms in Reinforcement Learning｜强化学习中的探索机制
Imitation Learning｜模仿学习
Offiline Reinforcement Learning｜离线强化学习
Model-Based Reinforcement Learning｜基于模型的强化学习
means other sub-direction algorithms, usually as plugin-in in the whole pipeline
P.S: The .py file in Runnable Demo can be found in dizoo
Environment Versatility
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README 内容过长，已截取前 64 KB。完整内容请前往 GitHub 仓库查看。
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