# DI-engine

**Repository Path**: gujinjiang/DI-engine

## Basic Information

- **Project Name**: DI-engine
- **Description**: OpenDILab决策智能引擎
- **Primary Language**: Python
- **License**: Apache-2.0
- **Default Branch**: main
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 2
- **Forks**: 24
- **Created**: 2021-10-19
- **Last Updated**: 2023-01-07

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

<div align="center">
    <a href="http://di-engine.github.io"><img width="500px" height="auto" src="https://github.com/opendilab/DI-engine-docs/blob/main/source/images/di_engine_logo.svg"></a>
</div>

---

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Updated on 2021.09.30 DI-engine-v0.2.0 (beta)


## Introduction to DI-engine (beta)
DI-engine is a generalized Decision Intelligence engine. It supports most basic deep reinforcement learning (DRL) algorithms, such as DQN, PPO, SAC, and domain-specific algorithms like QMIX in multi-agent RL, GAIL in inverse RL, and RND in exploration problems. Various training pipelines and customized decision AI applications are also supported. Have fun with exploration and exploitation.

### Application
- [DI-star](https://github.com/opendilab/DI-star)
- [DI-drive](https://github.com/opendilab/DI-drive)

### Environment
- [GoBigger](https://github.com/opendilab/GoBigger)

### System Optimization and Design
- [DI-orchestrator](https://github.com/opendilab/DI-orchestrator)
- [DI-hpc](https://github.com/opendilab/DI-hpc)
- [DI-store](https://github.com/opendilab/DI-store)

### Other
- [DI-engine-docs](https://github.com/opendilab/DI-engine-docs)
- [treevalue](https://github.com/opendilab/treevalue)
- [DI-treetensor](https://github.com/opendilab/DI-treetensor) (preview)

## Installation

You can simply install DI-engine from PyPI with the following command:
```bash
pip install DI-engine
```

If you use Anaconda or Miniconda, you can install DI-engine from conda-forge through the following command:
```bash
conda install -c opendilab di-engine
```

For more information about installation, you can refer to [installation](https://opendilab.github.io/DI-engine/installation/index.html).

And our dockerhub repo can be found [here](https://hub.docker.com/repository/docker/opendilab/ding)，we prepare `base image` and `env image` with common RL environments.

- base: opendilab/ding:nightly
- atari: opendilab/ding:nightly-atari
- mujoco: opendilab/ding:nightly-mujoco
- smac: opendilab/ding:nightly-smac

## Documentation

The detailed documentation are hosted on [doc](https://opendilab.github.io/DI-engine/)([中文文档](https://di-engine-docs.readthedocs.io/en/main-zh/)).

## Quick Start

[3 Minutes Kickoff](https://opendilab.github.io/DI-engine/quick_start/index.html)

[3 Minutes Kickoff(colab)](https://colab.research.google.com/drive/1J29voOD2v9_FXjW-EyTVfRxY_Op_ygef#scrollTo=MIaKQqaZCpGz)

[3 分钟上手中文版(kaggle)](https://www.kaggle.com/shenzhenperson/di-engine)

**Bonus: Train RL agent in one line code:**
```bash
ding -m serial -e cartpole -p dqn -s 0
```

## Feature

### Algorithm Versatility

|  No  |                          Algorithm                           |                            Label                             |                        Implementation                        |                        Runnable Demo                         |
| :--: | :----------------------------------------------------------: | :----------------------------------------------------------: | :----------------------------------------------------------: | :----------------------------------------------------------: |
|  1   | [DQN](https://storage.googleapis.com/deepmind-media/dqn/DQNNaturePaper.pdf) | ![discrete](https://img.shields.io/badge/-discrete-brightgreen) | [policy/dqn](https://github.com/opendilab/DI-engine/blob/main/ding/policy/dqn.py) | python3 -u cartpole_dqn_main.py / ding -m serial -c cartpole_dqn_config.py -s 0 |
|  2   |         [C51](https://arxiv.org/pdf/1707.06887.pdf)          | ![discrete](https://img.shields.io/badge/-discrete-brightgreen) | [policy/c51](https://github.com/opendilab/DI-engine/blob/main/ding/policy/c51.py) |        ding -m serial -c cartpole_c51_config.py -s 0         |
|  3   |       [QRDQN](https://arxiv.org/pdf/1710.10044.pdf)        | ![discrete](https://img.shields.io/badge/-discrete-brightgreen) | [policy/qrdqn](https://github.com/opendilab/DI-engine/blob/main/ding/policy/qrdqn.py) |       ding -m serial -c cartpole_qrdqn_config.py -s 0        |
|  4   |         [IQN](https://arxiv.org/pdf/1806.06923.pdf)          | ![discrete](https://img.shields.io/badge/-discrete-brightgreen) | [policy/iqn](https://github.com/opendilab/DI-engine/blob/main/ding/policy/iqn.py) |        ding -m serial -c cartpole_iqn_config.py -s 0         |
|  5   |         [Rainbow](https://arxiv.org/pdf/1710.02298.pdf)          | ![discrete](https://img.shields.io/badge/-discrete-brightgreen) | [policy/rainbow](https://github.com/opendilab/DI-engine/blob/main/ding/policy/rainbow.py) |      ding -m serial -c cartpole_rainbow_config.py -s 0       |
|  6   |         [SQL](https://arxiv.org/pdf/1702.08165.pdf)          | ![discrete](https://img.shields.io/badge/-discrete-brightgreen)![continuous](https://img.shields.io/badge/-continous-green) | [policy/sql](https://github.com/opendilab/DI-engine/blob/main/ding/policy/sql.py) |        ding -m serial -c cartpole_sql_config.py -s 0         |
|  7   |      [R2D2](https://openreview.net/forum?id=r1lyTjAqYX)      | ![dist](https://img.shields.io/badge/-distributed-blue)![discrete](https://img.shields.io/badge/-discrete-brightgreen) | [policy/r2d2](https://github.com/opendilab/DI-engine/blob/main/ding/policy/r2d2.py) |        ding -m serial -c cartpole_r2d2_config.py -s 0        |
|  8   |           [A2C](https://arxiv.org/pdf/1602.01783.pdf)            | ![discrete](https://img.shields.io/badge/-discrete-brightgreen) | [policy/a2c](https://github.com/opendilab/DI-engine/blob/main/ding/policy/a2c.py) |        ding -m serial -c cartpole_a2c_config.py -s 0         |
|  9   |           [PPO](https://arxiv.org/abs/1707.06347)            | ![discrete](https://img.shields.io/badge/-discrete-brightgreen)![continuous](https://img.shields.io/badge/-continous-green) | [policy/ppo](https://github.com/opendilab/DI-engine/blob/main/ding/policy/ppo.py) | python3 -u cartpole_ppo_main.py / ding -m serial_onpolicy -c cartpole_ppo_config.py -s 0 |
|  10  |         [PPG](https://arxiv.org/pdf/2009.04416.pdf)          | ![discrete](https://img.shields.io/badge/-discrete-brightgreen) | [policy/ppg](https://github.com/opendilab/DI-engine/blob/main/ding/policy/ppg.py) |               python3 -u cartpole_ppg_main.py                |
|  11  |         [ACER](https://arxiv.org/pdf/1611.01224.pdf)         | ![discrete](https://img.shields.io/badge/-discrete-brightgreen)![continuous](https://img.shields.io/badge/-continous-green) | [policy/acer](https://github.com/opendilab/DI-engine/blob/main/ding/policy/acer.py) |        ding -m serial -c cartpole_acer_config.py -s 0        |
|  12  |          [IMPALA](https://arxiv.org/abs/1802.01561)          | ![dist](https://img.shields.io/badge/-distributed-blue)![discrete](https://img.shields.io/badge/-discrete-brightgreen) | [policy/impala](https://github.com/opendilab/DI-engine/blob/main/ding/policy/impala.py) |       ding -m serial -c cartpole_impala_config.py -s 0       |
|  13  |         [DDPG](https://arxiv.org/pdf/1509.02971.pdf)         | ![continuous](https://img.shields.io/badge/-continous-green) | [policy/ddpg](https://github.com/opendilab/DI-engine/blob/main/ding/policy/ddpg.py) |        ding -m serial -c pendulum_ddpg_config.py -s 0        |
|  14  |         [TD3](https://arxiv.org/pdf/1802.09477.pdf)          | ![continuous](https://img.shields.io/badge/-continous-green) | [policy/td3](https://github.com/opendilab/DI-engine/blob/main/ding/policy/td3.py) | python3 -u pendulum_td3_main.py / ding -m serial -c pendulum_td3_config.py -s 0 |
|  15  |           [SAC](https://arxiv.org/abs/1801.01290)            | ![continuous](https://img.shields.io/badge/-continous-green) | [policy/sac](https://github.com/opendilab/DI-engine/blob/main/ding/policy/sac.py) |        ding -m serial -c pendulum_sac_config.py -s 0         |
|  16  |           [QMIX](https://arxiv.org/pdf/1803.11485.pdf)           |      ![MARL](https://img.shields.io/badge/-MARL-yellow)      | [policy/qmix](https://github.com/opendilab/DI-engine/blob/main/ding/policy/qmix.py) |       ding -m serial -c smac_3s5z_qmix_config.py -s 0        |
|  17  |         [COMA](https://arxiv.org/pdf/1705.08926.pdf)         |      ![MARL](https://img.shields.io/badge/-MARL-yellow)      | [policy/coma](https://github.com/opendilab/DI-engine/blob/main/ding/policy/coma.py) |       ding -m serial -c smac_3s5z_coma_config.py -s 0        |
|  18  |          [QTran](https://arxiv.org/abs/1905.05408)           |      ![MARL](https://img.shields.io/badge/-MARL-yellow)      | [policy/qtran](https://github.com/opendilab/DI-engine/blob/main/ding/policy/qtran.py) |       ding -m serial -c smac_3s5z_qtran_config.py -s 0       |
|  19  |          [WQMIX](https://arxiv.org/abs/2006.10800)           |      ![MARL](https://img.shields.io/badge/-MARL-yellow)      | [policy/wqmix](https://github.com/opendilab/DI-engine/blob/main/ding/policy/wqmix.py) |       ding -m serial -c smac_3s5z_wqmix_config.py -s 0       |
|  20  |        [CollaQ](https://arxiv.org/pdf/2010.08531.pdf)        |      ![MARL](https://img.shields.io/badge/-MARL-yellow)      | [policy/collaq](https://github.com/opendilab/DI-engine/blob/main/ding/policy/collaq.py) |      ding -m serial -c smac_3s5z_collaq_config.py -s 0       |
|  21  |           [GAIL](https://arxiv.org/pdf/1606.03476.pdf)           |        ![IL](https://img.shields.io/badge/-IL-purple)        | [reward_model/gail](https://github.com/opendilab/DI-engine/blob/main/ding/reward_model/gail_irl_model.py) |  ding -m serial_reward_model -c cartpole_dqn_config.py -s 0  |
|  22  |         [SQIL](https://arxiv.org/pdf/1905.11108.pdf)         |        ![IL](https://img.shields.io/badge/-IL-purple)        | [entry/sqil](https://github.com/opendilab/DI-engine/blob/main/ding/entry/serial_entry_sqil.py) |     ding -m serial_sqil -c cartpole_sqil_config.py -s 0      |
|  23  |           [HER](https://arxiv.org/pdf/1707.01495.pdf)            |   ![exp](https://img.shields.io/badge/-exploration-orange)   | [reward_model/her](https://github.com/opendilab/DI-engine/blob/main/ding/reward_model/her_reward_model.py) |                python3 -u bitflip_her_dqn.py                 |
|  24  |           [RND](https://arxiv.org/abs/1810.12894)            |   ![exp](https://img.shields.io/badge/-exploration-orange)   | [reward_model/rnd](https://github.com/opendilab/DI-engine/blob/main/ding/reward_model/rnd_reward_model.py) |             python3 -u cartpole_ppo_rnd_main.py              |
|  25  |         [CQL](https://arxiv.org/pdf/2006.04779.pdf)          | ![offline](https://img.shields.io/badge/-offlineRL-darkblue) | [policy/cql](https://github.com/opendilab/DI-engine/blob/main/ding/policy/cql.py) |                 python3 -u d4rl_cql_main.py                  |
|  26  |         [PER](https://arxiv.org/pdf/1511.05952.pdf)          |   ![other](https://img.shields.io/badge/-other-lightgrey)    | [worker/replay_buffer](https://github.com/opendilab/DI-engine/blob/main/ding/worker/replay_buffer/advanced_buffer.py) |                        `rainbow demo`                        |
|  27  |         [GAE](https://arxiv.org/pdf/1506.02438.pdf)          |   ![other](https://img.shields.io/badge/-other-lightgrey)    | [rl_utils/gae](https://github.com/opendilab/DI-engine/blob/main/ding/rl_utils/gae.py) |                          `ppo demo`                          |
|  28  |         [D4PG](https://arxiv.org/pdf/1804.08617.pdf)         | ![continuous](https://img.shields.io/badge/-continous-green) | [policy/d4pg](https://github.com/opendilab/DI-engine/blob/main/ding/policy/d4pg.py) |        python3 -u pendulum_d4pg_config.py     |

![discrete](https://img.shields.io/badge/-discrete-brightgreen) means discrete action space, which is only label in normal DRL algorithms(1-15)

![continuous](https://img.shields.io/badge/-continous-green) means continuous action space, which is only label in normal DRL algorithms(1-15)

![dist](https://img.shields.io/badge/-distributed-blue) means distributed training (collector-learner parallel) RL algorithm

![MARL](https://img.shields.io/badge/-MARL-yellow) means multi-agent RL algorithm

![exp](https://img.shields.io/badge/-exploration-orange) means RL algorithm which is related to exploration and sparse reward

![IL](https://img.shields.io/badge/-IL-purple) means Imitation Learning, including Behaviour Cloning, Inverse RL, Adversarial Structured IL

![offline](https://img.shields.io/badge/-offlineRL-darkblue) means offline RL algorithm

![other](https://img.shields.io/badge/-other-lightgrey) means other sub-direction algorithm, usually as plugin-in in the whole pipeline

P.S: The `.py` file in `Runnable Demo` can be found in `dizoo`


### Environment Versatility
|  No  |                Environment               |                 Label               |         Visualization            |                   dizoo link                              |
| :--: | :--------------------------------------: | :---------------------------------: | :--------------------------------:|:---------------------------------------------------------: |
|  1   |       [atari](https://github.com/openai/gym/tree/master/gym/envs/atari)    | ![discrete](https://img.shields.io/badge/-discrete-brightgreen)   | ![original](./dizoo/atari/atari.gif)     |             [dizoo link](https://github.com/opendilab/DI-engine/tree/main/dizoo/atari/envs)        |
|  2   |       [box2d/bipedalwalker](https://github.com/openai/gym/tree/master/gym/envs/box2d)    | ![continuous](https://img.shields.io/badge/-continous-green) | ![original](./dizoo/box2d/bipedalwalker/original.gif)        | [dizoo link](https://github.com/opendilab/DI-engine/tree/main/dizoo/box2d/bipedalwalker/envs)         |
|  3   |       [box2d/lunarlander](https://github.com/openai/gym/tree/master/gym/envs/box2d)      | ![discrete](https://img.shields.io/badge/-discrete-brightgreen)   | ![original](./dizoo/box2d/lunarlander/lunarlander.gif)   |  [dizoo link](https://github.com/opendilab/DI-engine/tree/main/dizoo/box2d/lunarlander/envs)              |
|  4   |       [classic_control/cartpole](https://github.com/openai/gym/tree/master/gym/envs/classic_control)       | ![discrete](https://img.shields.io/badge/-discrete-brightgreen)   | ![original](./dizoo/classic_control/cartpole/cartpole.gif)    | [dizoo link](https://github.com/opendilab/DI-engine/tree/main/dizoo/classic_control/cartpole/envs)      |
|  5   |       [classic_control/pendulum](https://github.com/openai/gym/tree/master/gym/envs/classic_control)       | ![discrete](https://img.shields.io/badge/-discrete-brightgreen)   | ![original](./dizoo/classic_control/pendulum/pendulum.gif)    | [dizoo link](https://github.com/opendilab/DI-engine/tree/main/dizoo/classic_control/pendulum/envs)      |
|  6   |       [competitive_rl](https://github.com/cuhkrlcourse/competitive-rl)       | ![discrete](https://img.shields.io/badge/-discrete-brightgreen) ![selfplay](https://img.shields.io/badge/-selfplay-blue) | ![original](./dizoo/competitive_rl/competitive_rl.gif)   |  [dizoo link](https://github.com/opendilab/DI-engine/tree/main/dizoo.classic_control)   |
|  7   |       [gfootball](https://github.com/google-research/football)                        | ![discrete](https://img.shields.io/badge/-discrete-brightgreen)![sparse](https://img.shields.io/badge/-sparse%20reward-orange)![selfplay](https://img.shields.io/badge/-selfplay-blue) | ![original](./dizoo/gfootball/gfootball.gif)      | [dizoo link](https://github.com/opendilab/DI-engine/tree/main/dizoo.gfootball/envs)                   |
|  8   |       [minigrid](https://github.com/maximecb/gym-minigrid)                         | ![discrete](https://img.shields.io/badge/-discrete-brightgreen)![sparse](https://img.shields.io/badge/-sparse%20reward-orange) | ![original](./dizoo/minigrid/minigrid.gif)         | [dizoo link](https://github.com/opendilab/DI-engine/tree/main/dizoo/minigrid/envs)                      |
|  9   |       [mujoco](https://github.com/openai/gym/tree/master/gym/envs/mujoco)       |  ![continuous](https://img.shields.io/badge/-continous-green)  | ![original](./dizoo/mujoco/mujoco.gif)                    |     [dizoo link](https://github.com/opendilab/DI-engine/tree/main/dizoo/majoco/envs)                                       |
|  10   |       [multiagent_particle](https://github.com/openai/multiagent-particle-envs)         | ![discrete](https://img.shields.io/badge/-discrete-brightgreen) ![marl](https://img.shields.io/badge/-MARL-yellow)  | ![original](./dizoo/multiagent_particle/multiagent_particle.gif)     |  [dizoo link](https://github.com/opendilab/DI-engine/tree/main/dizoo/multiagent_particle/envs)        |
|  11   |       [overcooked](https://github.com/HumanCompatibleAI/overcooked-demo)     | ![discrete](https://img.shields.io/badge/-discrete-brightgreen) ![marl](https://img.shields.io/badge/-MARL-yellow)  | ![original](./dizoo/overcooked/overcooked.gif)       |   [dizoo link](https://github.com/opendilab/DI-engine/tree/main/dizoo/overcooded/envs)      |
|  12  |       [procgen](https://github.com/openai/procgen)                          | ![discrete](https://img.shields.io/badge/-discrete-brightgreen)   | ![original](./dizoo/procgen/coinrun/coinrun.gif) | [dizoo link](https://github.com/opendilab/DI-engine/tree/main/dizoo/procgen)       |
|  13  |       [pybullet](https://github.com/benelot/pybullet-gym)    | ![continuous](https://img.shields.io/badge/-continous-green)  | ![original](./dizoo/pybullet/pybullet.gif)       |  [dizoo link](https://github.com/opendilab/DI-engine/tree/main/dizoo/pybullet/envs)             |
|  14  |       [smac](https://github.com/oxwhirl/smac)     | ![discrete](https://img.shields.io/badge/-discrete-brightgreen) ![marl](https://img.shields.io/badge/-MARL-yellow)![selfplay](https://img.shields.io/badge/-selfplay-blue)![sparse](https://img.shields.io/badge/-sparse%20reward-orange) | ![original](./dizoo/smac/smac.gif)       |  [dizoo link](https://github.com/opendilab/DI-engine/tree/main/dizoo/smac/envs)                                         |
| 15 | [d4rl](https://github.com/rail-berkeley/d4rl) | ![offline](https://img.shields.io/badge/-offlineRL-darkblue) | ![ori](dizoo/d4rl/d4rl.gif) | [dizoo link](https://github.com/opendilab/DI-engine/tree/main/dizoo/d4rl) |
|  16  |       league_demo                      | ![discrete](https://img.shields.io/badge/-discrete-brightgreen) ![selfplay](https://img.shields.io/badge/-selfplay-blue) | ![original](./dizoo/league_demo/league_demo.png) | [dizoo link](https://github.com/opendilab/DI-engine/tree/main/dizoo/league_demo/envs)                |
|  17  |       pomdp atari                    | ![discrete](https://img.shields.io/badge/-discrete-brightgreen)   |  | [dizoo link](https://github.com/opendilab/DI-engine/tree/main/dizoo/pomdp/envs) |
|  18  |       [bsuite](https://github.com/deepmind/bsuite)                         | ![discrete](https://img.shields.io/badge/-discrete-brightgreen) | ![original](./dizoo/bsuite/bsuite.png) | [dizoo link](https://github.com/opendilab/DI-engine/tree/main/dizoo/bsuite/envs)  |
| 19 | [ImageNet](https://www.image-net.org/) | ![IL](https://img.shields.io/badge/-IL/SL-purple) | ![original](./dizoo/image_classification/imagenet.png) | [dizoo link](https://github.com/opendilab/DI-engine/tree/main/dizoo/image_classification) |
| 20 | [slime_volleyball](https://github.com/hardmaru/slimevolleygym) | ![discrete](https://img.shields.io/badge/-discrete-brightgreen)![selfplay](https://img.shields.io/badge/-selfplay-blue) | ![ori](dizoo/slime_volley/slime_volley.gif) | [dizoo link](https://github.com/opendilab/DI-engine/tree/main/dizoo/slime_volley) |

![discrete](https://img.shields.io/badge/-discrete-brightgreen) means discrete action space

![continuous](https://img.shields.io/badge/-continous-green) means continuous action space

![MARL](https://img.shields.io/badge/-MARL-yellow) means multi-agent RL environment

![sparse](https://img.shields.io/badge/-sparse%20reward-orange) means environment which is related to exploration and sparse reward

![offline](https://img.shields.io/badge/-offlineRL-darkblue) means offline RL environment

![IL](https://img.shields.io/badge/-IL/SL-purple) means Imitation Learning or Supervised Learning Dataset

![selfplay](https://img.shields.io/badge/-selfplay-blue) means environment that allows agent VS agent battle

P.S. some enviroments in Atari, such as **MontezumaRevenge**, are also sparse reward type

## Contribution

We appreciate all contributions to improve DI-engine, both algorithms and system designs. Please refer to CONTRIBUTING.md for more guides. And our roadmap can be accessed by [this link](https://github.com/opendilab/DI-engine/projects).

And users can join our [slack communication channel](https://join.slack.com/t/opendilab/shared_invite/zt-v9tmv4fp-nUBAQEH1_Kuyu_q4plBssQ) or our [forum](https://github.com/opendilab/DI-engine/discussions) for more detailed discussion.

For future plans or milestones, please refer to our [GitHub Projects](https://github.com/opendilab/DI-engine/projects).

## Citation
```latex
@misc{ding,
    title={{DI-engine: OpenDILab} Decision Intelligence Engine},
    author={DI-engine Contributors},
    publisher = {GitHub},
    howpublished = {\url{https://github.com/opendilab/DI-engine}},
    year={2021},
}
```

## License
DI-engine released under the Apache 2.0 license.