# DI-engine
**Repository Path**: acrowise/DI-engine
## Basic Information
- **Project Name**: DI-engine
- **Description**: OpenDILab决策智能引擎 https://github.com/opendilab/DI-engine
- **Primary Language**: Python
- **License**: Apache-2.0
- **Default Branch**: main
- **Homepage**: None
- **GVP Project**: No
## Statistics
- **Stars**: 0
- **Forks**: 24
- **Created**: 2022-05-10
- **Last Updated**: 2022-05-10
## Categories & Tags
**Categories**: Uncategorized
**Tags**: None
## README
---
[](https://twitter.com/opendilab)
[](https://pypi.org/project/DI-engine/)
[](https://codecov.io/gh/opendilab/DI-engine)
[](https://github.com/opendilab/DI-engine/stargazers)
[](https://github.com/opendilab/DI-engine/network)
[](https://github.com/opendilab/DI-engine/issues)
[](https://github.com/opendilab/DI-engine/pulls)
[](https://github.com/opendilab/DI-engine/graphs/contributors)
[](https://github.com/opendilab/DI-engine/blob/master/LICENSE)
Updated on 2022.04.22 DI-engine-v0.3.1
## Introduction to DI-engine (beta)
[DI-engine doc](https://di-engine-docs.readthedocs.io/en/latest/) | [中文文档](https://di-engine-docs.readthedocs.io/zh_CN/latest/)
**DI-engine** is a generalized decision intelligence engine. It supports **various [deep reinforcement learning](https://di-engine-docs.readthedocs.io/en/latest/intro_rl/basic.html) algorithms** ([link](https://github.com/opendilab/DI-engine#algorithm-versatility)):
- Most basic DRL algorithms, such as DQN, PPO, SAC, R2D2
- Multi-agent RL algorithms like QMIX, MAPPO
- Imitation learning algorithms (BC/IRL/GAIL) , such as GAIL, SQIL, Guided Cost Learning
- Exploration algorithms like HER, RND, ICM
- Offline RL algorithms: CQL, TD3BC
- Model-based RL algorithms: MBPO
**DI-engine** aims to **standardize different RL enviroments and applications**. Various training pipelines and customized decision AI applications are also supported.
- Traditional academic environments
- [DI-zoo](https://github.com/opendilab/DI-engine#environment-versatility)
- Real world decision AI applications
- [DI-star](https://github.com/opendilab/DI-star): Decision AI in StarCraftII
- [DI-drive](https://github.com/opendilab/DI-drive): Auto-driving platform
- [GoBigger](https://github.com/opendilab/GoBigger): Multi-Agent Decision Intelligence Environment
- [DI-smartcross](https://github.com/opendilab/DI-smartcross): Decision AI in Traffic Light Control
- General nested data lib
- [treevalue](https://github.com/opendilab/treevalue): Tree-nested data structure
- [DI-treetensor](https://github.com/opendilab/DI-treetensor): Tree-nested PyTorch tensor Lib
- Docs and Tutorials
- [DI-engine-docs](https://github.com/opendilab/DI-engine-docs)
- [awesome-model-based-RL](https://github.com/opendilab/awesome-model-based-RL): A curated list of awesome Model-Based RL resources
**DI-engine** also has some **system optimization and design** for efficient and robust large-scale RL training:
- [DI-orchestrator](https://github.com/opendilab/DI-orchestrator): RL Kubernetes Custom Resource and Operator Lib
- [DI-hpc](https://github.com/opendilab/DI-hpc): RL HPC OP Lib
- [DI-store](https://github.com/opendilab/DI-store): RL Object Store
Have fun with exploration and exploitation.
## 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://di-engine-docs.readthedocs.io/en/latest/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
The detailed documentation are hosted on [doc](https://di-engine-docs.readthedocs.io/en/latest/) | [中文文档](https://di-engine-docs.readthedocs.io/zh_CN/latest/).
## Quick Start
[3 Minutes Kickoff](https://di-engine-docs.readthedocs.io/en/latest/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/fallinx/di-engine/)
[How to migrate a new **RL Env**](https://di-engine-docs.readthedocs.io/en/latest/best_practice/ding_env.html) | [如何迁移一个新的**强化学习环境**](https://di-engine-docs.readthedocs.io/zh_CN/latest/best_practice/ding_env_zh.html)
**Bonus: Train RL agent in one line code:**
```bash
ding -m serial -e cartpole -p dqn -s 0
```
## Supporters
### ↳ Stargazers
[](https://github.com/opendilab/DI-engine/stargazers)
### ↳ Forkers
[](https://github.com/opendilab/DI-engine/network/members)
## Feature
### Algorithm Versatility
| No | Algorithm | Label | Doc and Implementation | Runnable Demo |
| :--: | :----------------------------------------------------------: | :----------------------------------------------------------: | :----------------------------------------------------------: | :----------------------------------------------------------: |
| 1 | [DQN](https://storage.googleapis.com/deepmind-media/dqn/DQNNaturePaper.pdf) |  | [DQN doc](https://di-engine-docs.readthedocs.io/en/latest/hands_on/dqn.html)
[DQN中文文档](https://di-engine-docs.readthedocs.io/zh_CN/latest/hands_on/dqn_zh.html)
[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) |  | [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) |  | [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) |  | [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) |  | [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) |  | [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) |  | [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) |  | [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)/[MAPPO](https://arxiv.org/pdf/2103.01955.pdf) |  | [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) |  | [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) |  | [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) |  | [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)/[PADDPG](https://arxiv.org/pdf/1511.04143.pdf) |  | [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) |  | [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 | [D4PG](https://arxiv.org/pdf/1804.08617.pdf) |  | [policy/d4pg](https://github.com/opendilab/DI-engine/blob/main/ding/policy/d4pg.py) | python3 -u pendulum_d4pg_config.py |
| 16 | [SAC](https://arxiv.org/abs/1801.01290) |  | [policy/sac](https://github.com/opendilab/DI-engine/blob/main/ding/policy/sac.py) | ding -m serial -c pendulum_sac_config.py -s 0 |
| 17 | [PDQN](https://arxiv.org/pdf/1810.06394.pdf) |  | [policy/pdqn](https://github.com/opendilab/DI-engine/blob/main/ding/policy/pdqn.py) | ding -m serial -c gym_hybrid_pdqn_config.py -s 0 |
| 18 | [MPDQN](https://arxiv.org/pdf/1905.04388.pdf) |  | [policy/pdqn](https://github.com/opendilab/DI-engine/blob/main/ding/policy/pdqn.py) | ding -m serial -c gym_hybrid_mpdqn_config.py -s 0 |
| 19 | [HPPO](https://arxiv.org/pdf/1903.01344.pdf) |  | [policy/ppo](https://github.com/opendilab/DI-engine/blob/main/ding/policy/ppo.py) | ding -m serial_onpolicy -c gym_hybrid_hppo_config.py -s 0 |
| 19 | [QMIX](https://arxiv.org/pdf/1803.11485.pdf) |  | [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 |
| 20 | [COMA](https://arxiv.org/pdf/1705.08926.pdf) |  | [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 |
| 21 | [QTran](https://arxiv.org/abs/1905.05408) |  | [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 |
| 22 | [WQMIX](https://arxiv.org/abs/2006.10800) |  | [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 |
| 23 | [CollaQ](https://arxiv.org/pdf/2010.08531.pdf) |  | [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 |
| 24 | [GAIL](https://arxiv.org/pdf/1606.03476.pdf) |  | [reward_model/gail](https://github.com/opendilab/DI-engine/blob/main/ding/reward_model/gail_irl_model.py) | ding -m serial_gail -c cartpole_dqn_gail_config.py -s 0 |
| 25 | [SQIL](https://arxiv.org/pdf/1905.11108.pdf) |  | [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 |
| 26 | [DQFD](https://arxiv.org/pdf/1704.03732.pdf) |  | [policy/dqfd](https://github.com/opendilab/DI-engine/blob/main/ding/policy/dqfd.py) | ding -m serial_dqfd -c cartpole_dqfd_config.py -s 0 |
| 27 | [R2D3](https://arxiv.org/pdf/1909.01387.pdf) |  | [R2D3中文文档](https://di-engine-docs.readthedocs.io/zh_CN/latest/hands_on/r2d3_zh.html)
[policy/r2d3](https://github.com/opendilab/DI-engine/blob/main/ding/policy/r2d3.py) | python3 -u pong_r2d3_r2d2expert_config.py |
| 28 | [Guided Cost Learning](https://arxiv.org/pdf/1603.00448.pdf) |  | [reward_model/guided_cost](https://github.com/opendilab/DI-engine/blob/main/ding/reward_model/guided_cost_reward_model.py) | python3 lunarlander_gcl_config.py |
| 29 | [TREX](https://arxiv.org/abs/1904.06387) |  | [reward_model/trex](https://github.com/opendilab/DI-engine/blob/main/ding/reward_model/trex_reward_model.py) | python3 mujoco_trex_main.py |
| 30 | [HER](https://arxiv.org/pdf/1707.01495.pdf) |  | [reward_model/her](https://github.com/opendilab/DI-engine/blob/main/ding/reward_model/her_reward_model.py) | python3 -u bitflip_her_dqn.py |
| 31 | [RND](https://arxiv.org/abs/1810.12894) |  | [reward_model/rnd](https://github.com/opendilab/DI-engine/blob/main/ding/reward_model/rnd_reward_model.py) | python3 -u cartpole_rnd_onppo_config.py |
| 32 | [ICM](https://arxiv.org/pdf/1705.05363.pdf) |  | [ICM中文文档](https://di-engine-docs.readthedocs.io/zh_CN/latest/hands_on/icm_zh.html)
[reward_model/icm](https://github.com/opendilab/DI-engine/blob/main/ding/reward_model/icm_reward_model.py) | python3 -u cartpole_ppo_icm_config.py |
| 33 | [CQL](https://arxiv.org/pdf/2006.04779.pdf) |  | [policy/cql](https://github.com/opendilab/DI-engine/blob/main/ding/policy/cql.py) | python3 -u d4rl_cql_main.py |
| 34 | [TD3BC](https://arxiv.org/pdf/2106.06860.pdf) |  | [policy/td3_bc](https://github.com/opendilab/DI-engine/blob/main/ding/policy/td3_bc.py) | python3 -u mujoco_td3_bc_main.py |
| 35 | [MBPO](https://arxiv.org/pdf/1906.08253.pdf) |  | [model/template/model_based/mbpo](https://github.com/opendilab/DI-engine/blob/main/ding/model/template/model_based/mbpo.py) | python3 -u sac_halfcheetah_mopo_default_config.py |
| 36 | [PER](https://arxiv.org/pdf/1511.05952.pdf) |  | [worker/replay_buffer](https://github.com/opendilab/DI-engine/blob/main/ding/worker/replay_buffer/advanced_buffer.py) | `rainbow demo` |
| 37 | [GAE](https://arxiv.org/pdf/1506.02438.pdf) |  | [rl_utils/gae](https://github.com/opendilab/DI-engine/blob/main/ding/rl_utils/gae.py) | `ppo demo` |
 means discrete action space, which is only label in normal DRL algorithms (1-18)
 means continuous action space, which is only label in normal DRL algorithms (1-18)
 means hybrid (discrete + continuous) action space (1-18)
 means distributed training (collector-learner parallel) RL algorithm
 means multi-agent RL algorithm
 means RL algorithm which is related to exploration and sparse reward
 means Imitation Learning, including Behaviour Cloning, Inverse RL, Adversarial Structured IL
 means offline RL algorithm
 means model-based RL algorithm
 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 | Code and Doc Links |
| :--: | :--------------------------------------: | :---------------------------------: | :--------------------------------:|:---------------------------------------------------------: |
| 1 | [atari](https://github.com/openai/gym/tree/master/gym/envs/atari) |  |  | [code link](https://github.com/opendilab/DI-engine/tree/main/dizoo/atari/envs)
[env tutorial](https://di-engine-docs.readthedocs.io/en/latest/env_tutorial/atari.html)
[环境指南](https://di-engine-docs.readthedocs.io/zh_CN/latest/env_tutorial/atari_zh.html) |
| 2 | [box2d/bipedalwalker](https://github.com/openai/gym/tree/master/gym/envs/box2d) |  |  | [dizoo link](https://github.com/opendilab/DI-engine/tree/main/dizoo/box2d/bipedalwalker/envs)
[环境指南](https://di-engine-docs.readthedocs.io/zh_CN/latest/env_tutorial/bipedalwalker_zh.html) |
| 3 | [box2d/lunarlander](https://github.com/openai/gym/tree/master/gym/envs/box2d) |  |  | [dizoo link](https://github.com/opendilab/DI-engine/tree/main/dizoo/box2d/lunarlander/envs)
[环境指南](https://di-engine-docs.readthedocs.io/zh_CN/latest/env_tutorial/lunarlander_zh.html) |
| 4 | [classic_control/cartpole](https://github.com/openai/gym/tree/master/gym/envs/classic_control) |  |  | [dizoo link](https://github.com/opendilab/DI-engine/tree/main/dizoo/classic_control/cartpole/envs)
[环境指南](https://di-engine-docs.readthedocs.io/zh_CN/latest/env_tutorial/cartpole_zh.html) |
| 5 | [classic_control/pendulum](https://github.com/openai/gym/tree/master/gym/envs/classic_control) |  |  | [dizoo link](https://github.com/opendilab/DI-engine/tree/main/dizoo/classic_control/pendulum/envs)
[环境指南](https://di-engine-docs.readthedocs.io/zh_CN/latest/env_tutorial/pendulum_zh.html) |
| 6 | [competitive_rl](https://github.com/cuhkrlcourse/competitive-rl) |   |  | [dizoo link](https://github.com/opendilab/DI-engine/tree/main/dizoo.classic_control)
[环境指南](https://di-engine-docs.readthedocs.io/zh_CN/latest/env_tutorial/competitive_rl_zh.html) |
| 7 | [gfootball](https://github.com/google-research/football) |  |  | [dizoo link](https://github.com/opendilab/DI-engine/tree/main/dizoo.gfootball/envs)
[环境指南](https://di-engine-docs.readthedocs.io/zh_CN/latest/env_tutorial/gfootball_zh.html) |
| 8 | [minigrid](https://github.com/maximecb/gym-minigrid) |  |  | [dizoo link](https://github.com/opendilab/DI-engine/tree/main/dizoo/minigrid/envs)
[环境指南](https://di-engine-docs.readthedocs.io/zh_CN/latest/env_tutorial/minigrid_zh.html) |
| 9 | [mujoco](https://github.com/openai/gym/tree/master/gym/envs/mujoco) |  |  | [dizoo link](https://github.com/opendilab/DI-engine/tree/main/dizoo/majoco/envs)
[环境指南](https://di-engine-docs.readthedocs.io/zh_CN/latest/env_tutorial/mujoco_zh.html) |
| 10 | [PettingZoo](https://github.com/Farama-Foundation/PettingZoo) |    |  | [dizoo link](https://github.com/opendilab/DI-engine/tree/main/dizoo/petting_zoo/envs)
[环境指南](https://di-engine-docs.readthedocs.io/zh_CN/latest/env_tutorial/multiagent_particle_zh.html) |
| 11 | [overcooked](https://github.com/HumanCompatibleAI/overcooked-demo) |   |  | [dizoo link](https://github.com/opendilab/DI-engine/tree/main/dizoo/overcooded/envs)
[env tutorial](https://di-engine-docs.readthedocs.io/en/latest/env_tutorial/overcooked.html) |
| 12 | [procgen](https://github.com/openai/procgen) |  |  | [dizoo link](https://github.com/opendilab/DI-engine/tree/main/dizoo/procgen)
[环境指南](https://di-engine-docs.readthedocs.io/zh_CN/latest/env_tutorial/procgen_zh.html) |
| 13 | [pybullet](https://github.com/benelot/pybullet-gym) |  |  | [dizoo link](https://github.com/opendilab/DI-engine/tree/main/dizoo/pybullet/envs)
[环境指南](https://di-engine-docs.readthedocs.io/zh_CN/latest/env_tutorial/pybullet_zh.html) |
| 14 | [smac](https://github.com/oxwhirl/smac) |   |  | [dizoo link](https://github.com/opendilab/DI-engine/tree/main/dizoo/smac/envs)
[环境指南](https://di-engine-docs.readthedocs.io/zh_CN/latest/env_tutorial/smac_zh.html) |
| 15 | [d4rl](https://github.com/rail-berkeley/d4rl) |  |  | [dizoo link](https://github.com/opendilab/DI-engine/tree/main/dizoo/d4rl)
[环境指南](https://di-engine-docs.readthedocs.io/zh_CN/latest/env_tutorial/d4rl_zh.html) |
| 16 | league_demo |   |  | [dizoo link](https://github.com/opendilab/DI-engine/tree/main/dizoo/league_demo/envs) |
| 17 | pomdp atari |  | | [dizoo link](https://github.com/opendilab/DI-engine/tree/main/dizoo/pomdp/envs) |
| 18 | [bsuite](https://github.com/deepmind/bsuite) |  |  | [dizoo link](https://github.com/opendilab/DI-engine/tree/main/dizoo/bsuite/envs)
[env tutorial](https://di-engine-docs.readthedocs.io/en/latest/env_tutorial/bsuite.html) |
| 19 | [ImageNet](https://www.image-net.org/) |  |  | [dizoo link](https://github.com/opendilab/DI-engine/tree/main/dizoo/image_classification)
[环境指南](https://di-engine-docs.readthedocs.io/zh_CN/latest/env_tutorial/image_cls_zh.html) |
| 20 | [slime_volleyball](https://github.com/hardmaru/slimevolleygym) |  |  | [dizoo link](https://github.com/opendilab/DI-engine/tree/main/dizoo/slime_volley)
[env tutorial](https://di-engine-docs.readthedocs.io/en/latest/env_tutorial/slime_volleyball.html)
[环境指南](https://di-engine-docs.readthedocs.io/zh_CN/latest/env_tutorial/slime_volleyball_zh.html) |
| 21 | [gym_hybrid](https://github.com/thomashirtz/gym-hybrid) |  |  | [dizoo link](https://github.com/opendilab/DI-engine/tree/main/dizoo/gym_hybrid)
[环境指南](https://di-engine-docs.readthedocs.io/zh_CN/latest/env_tutorial/gym_hybrid_zh.html) |
| 22 | [GoBigger](https://github.com/opendilab/GoBigger) |  |  | [opendilab link](https://github.com/opendilab/GoBigger-Challenge-2021/tree/main/di_baseline)
[env tutorial](https://gobigger.readthedocs.io/en/latest/index.html)
[环境指南](https://gobigger.readthedocs.io/zh_CN/latest/) |
| 23 | [gym_soccer](https://github.com/openai/gym-soccer) |  |  | [dizoo link](https://github.com/opendilab/DI-engine/tree/main/dizoo/gym_soccer)
[环境指南](https://di-engine-docs.readthedocs.io/zh_CN/latest/env_tutorial/gym_soccer_zh.html) |
| 24 |[multiagent_mujoco](https://github.com/schroederdewitt/multiagent_mujoco) |   |  | [dizoo link](https://github.com/opendilab/DI-engine/tree/main/dizoo/multiagent_mujoco/envs)
[环境指南](https://di-engine-docs.readthedocs.io/zh_CN/latest/env_tutorial/mujoco_zh.html) |
| 25 |bitflip |   |  | [dizoo link](https://github.com/opendilab/DI-engine/tree/main/dizoo/bitflip/envs)
[环境指南](https://di-engine-docs.readthedocs.io/zh_CN/latest/env_tutorial/bitflip_zh.html) |
 means discrete action space
 means continuous action space
 means hybrid (discrete + continuous) action space
 means multi-agent RL environment
 means environment which is related to exploration and sparse reward
 means offline RL environment
 means Imitation Learning or Supervised Learning Dataset
 means environment that allows agent VS agent battle
P.S. some enviroments in Atari, such as **MontezumaRevenge**, are also sparse reward type
## Feedback and Contribution
- [File an issue](https://github.com/opendilab/DI-engine/issues/new/choose) on Github
- Open or participate in our [forum](https://github.com/opendilab/DI-engine/discussions)
- Discuss on DI-engine [slack communication channel](https://join.slack.com/t/opendilab/shared_invite/zt-v9tmv4fp-nUBAQEH1_Kuyu_q4plBssQ) or QQ group (700157520)
- Contributes to our future plan [Roadmap](https://github.com/opendilab/DI-engine/projects)
We appreciate all the feedbacks and contributions to improve DI-engine, both algorithms and system designs. And `CONTRIBUTING.md` offers some necessary information.
## 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.