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开源软件名称:DI-engine开源软件地址:https://gitee.com/opendilab/DI-engine开源软件介绍:Updated on 2022.03.24 DI-engine-v0.3.0 (beta) Introduction to DI-engine (beta)DI-engine is a generalized decision intelligence engine. It supports various deep reinforcement learning algorithms (link):
DI-engine aims to standardize different RL enviroments and applications. Various training pipelines and customized decision AI applications are also supported.
DI-engine also has some system optimization and design for efficient and robust large-scale RL training:
Have fun with exploration and exploitation. InstallationYou can simply install DI-engine from PyPI with the following command: pip install DI-engine If you use Anaconda or Miniconda, you can install DI-engine from conda-forge through the following command: conda install -c opendilab di-engine For more information about installation, you can refer to installation. And our dockerhub repo can be found here,we prepare
The detailed documentation are hosted on doc | 中文文档. Quick StartHow to migrate a new RL Env | 如何迁移一个新的强化学习环境 Bonus: Train RL agent in one line code: ding -m serial -e cartpole -p dqn -s 0 Supporters↳ Stargazers↳ ForkersFeatureAlgorithm Versatility
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 Environment Versatilitymeans 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
We appreciate all the feedbacks and contributions to improve DI-engine, both algorithms and system designs. And Citation@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},} LicenseDI-engine released under the Apache 2.0 license. |
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